Dr Eng-Jon Ong

The Prometheus project has investigated new ways of creating, distributing and displaying 3D television. The tools developed will also help today’s virtual studio production. 3D content is created by extension of the principles of a virtual studio to include realistic 3D representation of actors. Several techniques for this have been developed: • Texture-mapping of live video onto rough 3D actor models. • Fully-animated 3D avatars: • Photo-realistic body model generated from several still images of a person from different viewpoints. • Addition of a detailed head model taken from two close-up images of the head. • Tracking of face and body movements of a live performer using several cameras, to derive animation data which can be applied to the face and body. • Simulation of virtual clothing which can be applied to the animated avatars. MPEG-4 is used to distribute the content in its original 3D form. The 3D scene may be rendered in a form suitable for display on a ‘glasses-free’ 3D display, based on the principle of Integral Imaging. By assembling these elements in an end-to-end chain, the project has shown how a future 3D TV system could be realised. Furthermore, the tools developed will also improve the production methods available for conventional virtual studios, by focusing on sensor-free and markerless motion capture technology, methods for the rapid creation of photo-realistic virtual humans, and real-time clothing simulation.

This paper outlines a method of estimating the 3D pose of the upper human body from a single uncalibrated camera. The objective application lies in 3D Human Computer Interaction where hand depth information offers extended functionality when interacting with a 3D virtual environment, but it is equally suitable to animation and motion capture. A database of 3D body configurations is built from a variety of human movements using motion capture data. A hierarchical structure consisting of three subsidiary databases, namely the frontal-view Hand Position (top-level), Silhouette and Edge Map Databases, are pre-extracted from the 3D body configuration database. Using this hierarchy, subsets of the subsidiary databases are then matched to the subject in real-time. The examples of the subsidiary databases that yield the highest matching score are used to extract the corresponding 3D configuration from the motion capture data, thereby estimating the upper body 3D pose.

In this paper, we aim to tackle the problem of recognising temporal sequences in the context of a multi-class problem. In the past, the representation of sequential patterns was used for modelling discriminative temporal patterns for different classes. Here, we have improved on this by using the more general representation of episodes, of which sequential patterns are a special case. We then propose a novel tree structure called a MultI-Class Episode Tree (MICE-Tree) that allows one to simultaneously model a set of different episodes in an efficient manner whilst providing labels for them. A set of MICE-Trees are then combined together into a MICE-Forest that is learnt in a Boosting framework. The result is a strong classifier that utilises episodes for performing classification of temporal sequences. We also provide experimental evidence showing that the MICE-Trees allow for a more compact and efficient model compared to sequential patterns. Additionally, we demonstrate the accuracy and robustness of the proposed method in the presence of different levels of noise and class labels.

Meaningful Non-Verbal Communication (NVC) signals can be recognised by facial deformations based on video tracking. However, the geometric features previously used contain a significant amount of redundant or irrelevant information. A feature selection method is described for selecting a subset of features that improves performance and allows for the identification and visualisation of facial areas involved in NVC. The feature selection is based on a sequential backward elimination of features to find a effective subset of components. This results in a significant improvement in recognition performance, as well as providing evidence that brow lowering is involved in questioning sentences. The improvement in performance is a step towards a more practical automatic system and the facial areas identified provide some insight into human behaviour.

This paper proposes a non-linear predictor for estimating the displacement of tracked feature points on faces that exhibit significant variations across pose and expression. Existing methods such as linear predictors, ASMs or AAMs are limited to a narrow range in pose. In order to track across a large pose range, separate pose-specific models are required that are then coupled via a pose-estimator. In our approach, we neither require a set of pose-specific models nor a pose-estimator. Using just a single tracking model, we are able to robustly and accurately track across a wide range of expression on poses. This is achieved by gradient boosting of regression trees for predicting the displacement vectors of tracked points. Additionally, we propose a novel algorithm for simultaneously configuring this hierarchical set of trackers for optimal tracking results. Experiments were carried out on sequences of naturalistic conversation and sequences with large pose and expression changes. The results show that the proposed method is superior to state of the art methods, in being able to robustly track a set of facial points whilst gracefully recovering from tracking failures. © 2013 IEEE.

Distance functions are an important component in many learning applications. However, the correct function is context dependent, therefore it is advantageous to learn a distance function using available training data. Many existing distance functions is the requirement for data to exist in a space of constant dimensionality and not possible to be directly used on symbolic data. To address these problems, this paper introduces an alternative learnable distance function, based on multi-kernel distance bases or "wormholes that connects spaces belonging to similar examples that were originally far away close together. This work only assumes the availability of a set data in the form of relative comparisons, avoiding the need for having labelled or quantitative information. To learn the distance function, two algorithms were proposed: 1) Building a set of basic wormhole bases using a Boosting-inspired algorithm. 2) Merging different distance bases together for better generalisation. The learning algorithms were then shown to successfully extract suitable distance functions in various clustering problems, ranging from synthetic 2D data to symbolic representations of unlabelled images

Unravelling protein distributions within individual cells is vital to understanding their function and state and indispensable to developing new treatments. Here we present the Hybrid subCellular Protein Localiser (HCPL), which learns from weakly labelled data to robustly localise single-cell subcellular protein patterns. It comprises innovative DNN architectures exploiting wavelet filters and learnt parametric activations that successfully tackle drastic cell variability. HCPL features correlation-based ensembling of novel architectures that boosts performance and aids generalisation. Large-scale data annotation is made feasible by our AI-trains-AI approach, which determines the visual integrity of cells and emphasises reliable labels for efficient training. In the Human Protein Atlas context, we demonstrate that HCPL is best performing in the single-cell classification of protein localisation patterns. To better understand the inner workings of HCPL and assess its biological relevance, we analyse the contributions of each system component and dissect the emergent features from which the localisation predictions are derived. The Hybrid subCellular Protein Localiser (HCPL) improves single-cell classification for protein localization, allowing for large-scale data annotation through deep-learning architecture.

This paper attempts to tackle the problem of lipreading by building visual sequence classifiers that are based on salient temporal signatures. The temporal signatures used in this paper allow us to capture spatio-temporal information that can span multiple feature dimensions with gaps in the temporal axis. Selecting suitable temporal signatures by exhaustive search is not possible given the immensely large search space. As an example, the temporal sequence used in this paper would require exhaustively evaluating 2 2000 temporal signatures which is simply not possible. To address this, a novel gradient-descent based method is proposed to search for a suitable candidate temporal signature. Crucially, this is achieved very efficiently with O(nD) complexity, where D is the static feature vector dimensionality and n the maximum length of the temporal signatures considered. We then integrate this temporal search method into the AdaBoost algorithm. The results are spatio-temporal strong classifiers that can be applied to multi-class recognition in the lipreading domain. We provide experimental results evaluating the performance of our method against existing work in both subject dependent and subject independent cases demonstrating state of the art performance. Importantly, this was also achieved with a small set of temporal signatures.

This paper proposes a learnt data-driven approach for accurate, real-time tracking of facial features using only intensity information. Constraints such as a-priori shape models or temporal models for dynamics are not required or used. Tracking facial features simply becomes the independent tracking of a set of points on the face. This allows us to cope with facial configurations not present in the training data. Tracking is achieved via linear predictors which provide a fast and effective method for mapping pixel-level information to tracked feature position displacements. To improve on this, a novel and robust biased linear predictor is proposed in this paper. Multiple linear predictors are grouped into a rigid flock to increase robustness. To further improve tracking accuracy, a novel probabilistic selection method is used to identify relevant visual areas for tracking a feature point. These selected flocks are then combined into a hierarchical multi-resolution LP model. Experimental results also show that this method performs more robustly and accurately than AAMs, without any a priori shape information and with minimal training examples.

Existing work on animation synthesis can be roughly split into two approaches, those that combine segments of motion capture data, and those that perform inverse kinematics. In this paper, we present a method for performing animation synthesis of an articulated object (e.g. human body and a dog) from a minimal set of body joint positions, following the approach of inverse kinematics. We tackle this problem from a learning perspective. Firstly, we address the need for knowledge on the physical constraints of the articulated body, so as to avoid the generation of a physically impossible poses. A common solution is to heuristically specify the kinematic constraints for the skeleton model. In this paper however, the physical constraints of the articulated body are represented using a hierarchical cluster model learnt from a motion capture database. Additionally, we shall show that the learnt model automatically captures the correlation between different joints through the simultaneous modelling their angles. We then show how this model can be utilised to perform inverse kinematics in a simple and efficient manner. Crucially, we describe how IK is carried out from a minimal set of end-effector positions. Following this, we show how this "learnt inverse kinematics" framework can be used to perform animation syntheses of different types of articulated structures. To this end, the results presented include the retargeting of a at surface walking animation to various uneven terrains to demonstrate the synthesis of a full human body motion from the positions of only the hands, feet and torso. Additionally, we show how the same method can be applied to the animation synthesis of a dog using only its feet and torso positions.

This paper proposes a novel machine learning algorithm (SP-Boosting) to tackle the problem of lipreading by building visual sequence classifiers based on sequential patterns. We show that an exhaustive search of optimal sequential patterns is not possible due to the immense search space, and tackle this with a novel, efficient tree-search method with a set of pruning criteria. Crucially, the pruning strategies preserve our ability to locate the optimal sequential pattern. Additionally, the tree-based search method accounts for the training set’s boosting weight distribution. This temporal search method is then integrated into the boosting framework resulting in the SP-Boosting algorithm. We also propose a novel constrained set of strong classifiers that further improves recognition accuracy. The resulting learnt classifiers are applied to lipreading by performing multi-class recognition on the OuluVS database. Experimental results show that our method achieves state of the art recognition performane, using only a small set of sequential patterns.

Human lip-readers are increasingly being presented as useful in the gathering of forensic evidence but, like all humans, suffer from unreliability. Here we report the results of a long-term study in automatic lip-reading with the objective of converting video-to-text (V2T). The V2T problem is surprising in that some aspects that look tricky, such as real-time tracking of the lips on poor-quality interlaced video from hand-held cameras, but prove to be relatively tractable. Whereas the problem of speaker independent lip-reading is very demanding due to unpredictable variations between people. Here we review the problem of automatic lip-reading for crime fighting and identify the critical parts of the problem.

This paper proposes a clustered exemplar-based model for performing viewpoint invariant tracking of the 3D motion of a human subject from a single camera. Each exemplar is associated with multiple view visual information of a person and the corresponding 3D skeletal pose. The visual information takes the form of contours obtained from different viewpoints around the subject. The inclusion of multi-view information is important for two reasons: viewpoint invariance; and generalisation to novel motions. Visual tracking of human motion is performed using a particle filter coupled to the dynamics of human movement represented by the exemplar-based model. Dynamics are modelled by clustering 3D skeletal motions with similar movement and encoding the flow both within and between clusters. Results of single view tracking demonstrate that the exemplar-based models incorporating dynamics generalise to viewpoint invariant tracking of novel movements.

Recognition of human communication has previously focused on deliberately acted emotions or in structured or artificial social contexts. This makes the result hard to apply to realistic social situations. This paper describes the recording of spontaneous human communication in a specific and common social situation: conversation between two people. The clips are then annotated by multiple observers to reduce individual variations in interpretation of social signals. Temporal and static features are generated from tracking using heuristic and algorithmic methods. Optimal features for classifying examples of spontaneous communication signals are then extracted by AdaBoost. The performance of the boosted classifier is comparable to human performance for some communication signals, even on this challenging and realistic data set.

We describe a prototype Search-by-Example or look-up tool for signs, based on a newly developed 1000-concept sign lexicon for four national sign languages (GSL, DGS, LSF,BSL), which includes a spoken language gloss, a HamNoSys description, and a video for each sign. The look-up tool combines an interactive sign recognition system, supported by Kinect technology, with a real-time sign synthesis system,using a virtual human signer, to present results to the user. The user performs a sign to the system and is presented with animations of signs recognised as similar. The user also has the option to view any of these signs performed in the other three sign languages. We describe the supporting technology and architecture for this system, and present some preliminary evaluation results.

We propose a novel CNN architecture called ACTNET for robust instance image retrieval from large-scale datasets. Our key innovation is a learnable activation layer designed to improve the signal-to-noise ratio of deep convolutional feature maps. Further, we introduce a controlled multi-stream aggregation, where complementary deep features from different convolutional layers are optimally transformed and balanced using our novel activation layers, before aggregation into a global descriptor. Importantly, the learnable parameters of our activation blocks are explicitly trained, together with the CNN parameters, in an end-to-end manner minimising triplet loss. This means that our network jointly learns the CNN filters and their optimal activation and aggregation for retrieval tasks. To our knowledge, this is the first time parametric functions have been used to control and learn optimal multi-stream aggregation. We conduct an in-depth experimental study on three non-linear activation functions: Sine-Hyperbolic, Exponential and modified Weibull, showing that while all bring significant gains the Weibull function performs best thanks to its ability to equalise strong activations. The results clearly demonstrate that our ACTNET architecture significantly enhances the discriminative power of deep features, improving significantly over the state-of-the-art retrieval results on all datasets.

For automatic lipreading, there are many competing methods for feature extraction. Often, because of the complexity of the task these methods are tested on only quite restricted datasets, such as the letters of the alphabet or digits, and from only a few speakers. In this paper we compare some of the leading methods for lip feature extraction and compare them on the GRID dataset which uses a constrained vocabulary over, in this case, 15 speakers. Previously the GRID data has had restricted attention because of the requirements to track the face and lips accurately. We overcome this via the use of a novel linear predictor (LP) tracker which we use to control an Active Appearance Model (AAM). By ignoring shape and/or appearance parameters from the AAM we can quantify the effect of appearance and/or shape when lip-reading. We find that shape alone is a useful cue for lipreading (which is consistent with human experiments). However, the incremental effect of shape on appearance appears to be not significant which implies that the inner appearance of the mouth contains more information than the shape.

This paper introduces a novel approach to social behaviour recognition governed by the exchange of non-verbal cues between people. We conduct experiments to try and deduce distinct rules that dictate the social dynamics of people in a conversation, and utilise semi-supervised computer vision techniques to extract their social signals such as laughing and nodding. Data mining is used to deduce frequently occurring patterns of social trends between a speaker and listener in both interested and not interested social scenarios. The confidence values from rules are utilised to build a Social Dynamic Model (SDM), that can then be used for classification and visualisation. By visualising the rules generated in the SDM, we can analyse distinct social trends between an interested and not interested listener in a conversation. Results show that these distinctions can be applied generally and used to accurately predict conversational interest.

We introduce a new algorithm for real-time interactive motion control and demonstrate its application to motion captured data, pre-recorded videos and HCI. Firstly, a data set of frames are projected into a lower dimensional space. An appearance model is learnt using a multivariate probability distribution. A novel approach to determining transition points is presented based on k-medoids, whereby appropriate points of intersection in the motion trajectory are derived as cluster centres. These points are used to segment the data into smaller subsequences. A transition matrix combined with a kernel density estimation is used to determine suitable transitions between the subsequences to develop novel motion. To facilitate real-time interactive control, conditional probabilities are used to derive motion given user commands. The user commands can come from any modality including auditory, touch and gesture. The system is also extended to HCI using audio signals of speech in a conversation to trigger non-verbal responses from a synthetic listener in real-time. We demonstrate the flexibility of the model by presenting results ranging from data sets composed of vectorised images, 2D and 3D point representations. Results show real-time interaction and plausible motion generation between different types of movement.

This work addresses the problem of accurate semantic labelling of short videos. To this end, a multitude of different deep nets, ranging from traditional recurrent neural networks (LSTM, GRU), temporal agnostic networks (FV,VLAD,BoW), fully connected neural networks mid-stage AV fusion and others. Additionally, we also propose a residual architecture-based DNN for video classification, with state-of-the art classification performance at significantly reduced complexity. Furthermore, we propose four new approaches to diversity-driven multi-net ensembling, one based on fast correlation measure and three incorporating a DNN-based combiner. We show that significant performance gains can be achieved by ensembling diverse nets and we investigate factors contributing to high diversity. Based on the extensive YouTube8M dataset, we provide an in-depth evaluation and analysis of their behaviour. We show that the performance of the ensemble is state-of-the-art achieving the highest accuracy on the YouTube8M Kaggle test data. The performance of the ensemble of classifiers was also evaluated on the HMDB51 and UCF101 datasets, and show that the resulting method achieves comparable accuracy with state-ofthe- art methods using similar input features.

This paper proposes a novel machine learning algorithm (SP-Boosting) to tackle the problem of lipreading by building visual sequence classifiers based on sequential patterns. We show that an exhaustive search of optimal sequential patterns is not possible due to the immense search space, and tackle this with a novel, efficient tree-search method with a set of pruning criteria. Crucially, the pruning strategies preserve our ability to locate the optimal sequential pattern. Additionally, the tree-based search method accounts for the training set's boosting weight distribution. This temporal search method is then integrated into the boosting framework resulting in the SP-Boosting algorithm. We also propose a novel constrained set of strong classifiers that further improves recognition accuracy. The resulting learnt classifiers are applied to lipreading by performing multi-class recognition on the OuluVS database. Experimental results show that our method achieves state of the art recognition performane, using only a small set of sequential patterns. © 2011. The copyright of this document resides with its authors.

In this paper, we propose a computational model for social interaction between three people in a conversation, and demonstrate results using human video motion synthesis. We utilised semi-supervised computer vision techniques to label social signals between the people, like laughing, head nod and gaze direction. Data mining is used to deduce frequently occurring patterns of social signals between a speaker and a listener in both interested and not interested social scenarios, and the mined confidence values are used as conditional probabilities to animate social responses. The human video motion synthesis is done using an appearance model to learn a multivariate probability distribution, combined with a transition matrix to derive the likelihood of motion given a pose configuration. Our system uses social labels to more accurately define motion transitions and build a texture motion graph. Traditional motion synthesis algorithms are best suited to large human movements like walking and running, where motion variations are large and prominent. Our method focuses on generating more subtle human movement like head nods. The user can then control who speaks and the interest level of the individual listeners resulting in social interactive conversational agents.

This paper discusses sign language recognition using linguistic sub-units. It presents three types of sub-units for consideration; those learnt from appearance data as well as those inferred from both 2D or 3D tracking data. These sub-units are then combined using a sign level classifier; here, two options are presented. The first uses Markov Models to encode the temporal changes between sub-units. The second makes use of Sequential Pattern Boosting to apply discriminative feature selection at the same time as encoding temporal information. This approach is more robust to noise and performs well in signer independent tests, improving results from the 54% achieved by the Markov Chains to 76%.

This paper presents a flexible monocular system capable of recognising sign lexicons far greater in number than previous approaches. The power of the system is due to four key elements: (i) Head and hand detection based upon boosting which removes the need for temperamental colour segmentation; (ii) A body centred description of activity which overcomes issues with camera placement, calibration and user; (iii) A two stage classification in which stage I generates a high level linguistic description of activity which naturally generalises and hence reduces training; (iv) A stage II classifier bank which does not require HMMs, further reducing training requirements. The outcome of which is a system capable of running in real-time, and generating extremely high recognition rates for large lexicons with as little as a single training instance per sign. We demonstrate classification rates as high as 92% for a lexicon of 164 words with extremely low training requirements outperforming previous approaches where thousands of training examples are required.

This paper presents a method for learning distance functions of arbitrary feature representations that is based on the concept of wormholes. We introduce wormholes and describe how it provides a method for warping the topology of visual representation spaces such that a meaningful distance between examples is available. Additionally, we show how a more general distance function can be learnt through the combination of many wormholes via an inter-wormhole network. We then demonstrate the application of the distance learning method on a variety of problems including nonlinear synthetic data, face illumination detection and the retrieval of images containing natural landscapes and man-made objects (e.g. cities).

This chapter discusses sign language recognition using linguistic sub-units. It presents three types of sub-units for consideration; those learnt from appearance data as well as those inferred from both 2D or 3D tracking data. These sub-units are then combined using a sign level classifier; here, two options are presented. The first uses Markov Models to encode the temporal changes between sub-units. The second makes use of Sequential Pattern Boosting to apply discriminative feature selection at the same time as encoding temporal information. This approach is more robust to noise and performs well in signer independent tests, improving results from the 54% achieved by the Markov Chains to 76%.

This paper presents a novel, discriminative, multi-class classifier based on Sequential Pattern Trees. It is efficient to learn, compared to other Sequential Pattern methods, and scalable for use with large classifier banks. For these reasons it is well suited to Sign Language Recognition. Using deterministic robust features based on hand trajectories, sign level classifiers are built from sub-units. Results are presented both on a large lexicon single signer data set and a multi-signer Kinect™ data set. In both cases it is shown to out perform the non-discriminative Markov model approach and be equivalent to previous, more costly, Sequential Pattern (SP) techniques.

This paper proposes a learnt {____em data-driven} approach for accurate, real-time tracking of facial features using only intensity information, a non-trivial task since the face is a highly deformable object with large textural variations and motion in certain regions. The framework proposed here largely avoids the need for apriori design of feature trackers by automatically identifying the optimal visual support required for tracking a single facial feature point. This is essentially equivalent to automatically determining the visual context required for tracking. Tracking is achieved via biased linear predictors which provide a fast and effective method for mapping pixel-intensities into tracked feature position displacements. Multiple linear predictors are grouped into a rigid flock to increase robustness. To further improve tracking accuracy, a novel probabilistic selection method is used to identify relevant visual areas for tracking a feature point. These selected flocks are then combined into a hierarchical multi-resolution LP model. Finally, we also exploit a simple shape constraint for correcting the occasional tracking failure of a minority of feature points. Experimental results also show that this method performs more robustly and accurately than AAMs, on example sequences that range from SD quality to Youtube quality.

Human pose estimation in static images has received significant attention recently but the problem remains challenging. Using data acquired from a consumer depth sensor, our method combines a direct regression approach for the estimation of rigid body parts with the extraction of geodesic extrema to find extremities. We show how these approaches are complementary and present a novel approach to combine the results resulting in an improvement over the state-of-the-art. We report and compare our results a new dataset of aligned RGB-D pose sequences which we release as a benchmark for further evaluation. © 2013 IEEE.

We introduce a new algorithm for real-time interactive motion control and demonstrate its application to motion captured data, prerecorded videos, and HCI. Firstly, a data set of frames are projected into a lower dimensional space. An appearance model is learnt using a multivariate probability distribution. A novel approach to determining transition points is presented based on k-medoids, whereby appropriate points of intersection in the motion trajectory are derived as cluster centers. These points are used to segment the data into smaller subsequences. A transition matrix combined with a kernel density estimation is used to determine suitable transitions between the subsequences to develop novel motion. To facilitate real-time interactive control, conditional probabilities are used to derive motion given user commands. The user commands can come from any modality including auditory, touch, and gesture. The system is also extended to HCI using audio signals of speech in a conversation to trigger nonverbal responses from a synthetic listener in real-time. We demonstrate the flexibility of the model by presenting results ranging from data sets composed of vectorized images, 2-D, and 3-D point representations. Results show real-time interaction and plausible motion generation between different types of movement.

This paper tackles the problem of spotting a set of signs occuring in videos with sequences of signs. To achieve this, we propose to model the spatio-temporal signatures of a sign using an extension of sequential patterns that contain temporal intervals called Sequential Interval Patterns (SIP). We then propose a novel multi-class classifier that organises different sequential interval patterns in a hierarchical tree structure called a Hierarchical SIP Tree (HSP-Tree). This allows one to exploit any subsequence sharing that exists between different SIPs of different classes. Multiple trees are then combined together into a forest of HSP-Trees resulting in a strong classifier that can be used to spot signs. We then show how the HSP-Forest can be used to spot sequences of signs that occur in an input video. We have evaluated the method on both concatenated sequences of isolated signs and continuous sign sequences. We also show that the proposed method is superior in robustness and accuracy to a state of the art sign recogniser when applied to spotting a sequence of signs.

This paper addresses the problem of ultra-large-scale search in Hamming spaces. There has been considerable research on generating compact binary codes in vision, for example for visual search tasks. However the issue of efficient searching through huge sets of binary codes remains largely unsolved. To this end, we propose a novel, unsupervised approach to thresholded search in Hamming space, supporting long codes (e.g. 512-bits) with a wide-range of Hamming distance radii. Our method is capable of working efficiently with billions of codes delivering between one to three orders of magnitude acceleration, as compared to prior art. This is achieved by relaxing the equal-size constraint in the Multi-Index Hashing approach, leading to multiple hash-tables with variable length hash-keys. Based on the theoretical analysis of the retrieval probabilities of multiple hash-tables we propose a novel search algorithm for obtaining a suitable set of hash-key lengths. The resulting retrieval mechanism is shown empirically to improve the efficiency over the state-of-the-art, across a range of datasets, bit-depths and retrieval thresholds.

This paper presents a novel solution to the difficult task of both detecting and estimating the 3D pose of humans in monoscopic images. The approach consists of two parts. Firstly the location of a human is identified by a probabalistic assembly of detected body parts. Detectors for the face, torso and hands are learnt using adaBoost. A pose likliehood is then obtained using an a priori mixture model on body configuration and possible configurations assembled from available evidence using RANSAC. Once a human has been detected, the location is used to initialise a matching algorithm which matches the silhouette and edge map of a subject with a 3D model. This is done efficiently using chamfer matching, integral images and pose estimation from the initial detection stage. We demonstrate the application of the approach to large, cluttered natural images and at near framerate operation (16fps) on lower resolution video streams.

Recognition of non-verbal communication (NVC) is important for understanding human communication and designing user centric user interfaces. Cultural differences affect the expression and perception of NVC but no previous automatic system considers these cultural differences. Annotation data for the LILiR TwoTalk corpus, containing dyadic (two person) conversations, was gathered using Internet crowdsourcing, with a significant quantity collected from India, Kenya and the United Kingdom (UK). Many studies have investigated cultural differences based on human observations but this has not been addressed in the context of automatic emotion or NVC recognition. Perhaps not surprisingly, testing an automatic system on data that is not culturally representative of the training data is seen to result in low performance. We address this problem by training and testing our system on a specific culture to enable better modeling of the cultural differences in NVC perception. The system uses linear predictor tracking, with features generated based on distances between pairs of trackers. The annotations indicated the strength of the NVC which enables the use of v-SVR to perform the regression.

We propose a novel hybrid approach to static pose estimation called Connected Poselets. This representation combines the best aspects of part-based and example-based estimation. First detecting poselets extracted from the training data; our method then applies a modified Random Decision Forest to identify Poselet activations. By combining keypoint predictions from poselet activitions within a graphical model, we can infer the marginal distribution over each keypoint without any kinematic constraints. Our approach is demonstrated on a new publicly available dataset with promising results.