Representation and recognition of human actions in video

PhDAutomated human action recognition plays a critical role in the development of human-machine communication, by aiming for a more natural interaction between artificial intelligence and the human society. Recent developments in technology have permitted a shift from a traditional human action recognition performed in a well-constrained laboratory environment to realistic unconstrained scenarios. This advancement has given rise to new problems and challenges still not addressed by the available methods. Thus, the aim of this thesis is to study innovative approaches that address the challenging problems of human action recognition from video captured in unconstrained scenarios. To this end, novel action representations, feature selection methods, fusion strategies and classification approaches are formulated. More specifically, a novel interest points based action representation is firstly introduced, this representation seeks to describe actions as clouds of interest points accumulated at different temporal scales. The idea behind this method consists of extracting holistic features from the point clouds and explicitly and globally describing the spatial and temporal action dynamic. Since the proposed clouds of points representation exploits alternative and complementary information compared to the conventional interest points-based methods, a more solid representation is then obtained by fusing the two representations, adopting a Multiple Kernel Learning strategy. The validity of the proposed approach in recognising action from a well-known benchmark dataset is demonstrated as well as the superior performance achieved by fusing representations. Since the proposed method appears limited by the presence of a dynamic background and fast camera movements, a novel trajectory-based representation is formulated. Different from interest points, trajectories can simultaneously retain motion and appearance information even in noisy and crowded scenarios. Additionally, they can handle drastic camera movements and a robust region of interest estimation. An equally important contribution is the proposed collaborative feature selection performed to remove redundant and noisy components. In particular, a novel feature selection method based on Multi-Class Delta Latent Dirichlet Allocation (MC-DLDA) is introduced. Crucial, to enrich the final action representation, the trajectory representation is adaptively fused with a conventional interest point representation. The proposed approach is extensively validated on different datasets, and the reported performances are comparable with the best state-of-the-art. The obtained results also confirm the fundamental contribution of both collaborative feature selection and adaptive fusion. Finally, the problem of realistic human action classification in very ambiguous scenarios is taken into account. In these circumstances, standard feature selection methods and multi-class classifiers appear inadequate due to: sparse training set, high intra-class variation and inter-class similarity. Thus, both the feature selection and classification problems need to be redesigned. The proposed idea is to iteratively decompose the classification task in subtasks and select the optimal feature set and classifier in accordance with the subtask context. To this end, a cascaded feature selection and action classification approach is introduced. The proposed cascade aims to classify actions by exploiting as much information as possible, and at the same time trying to simplify the multi-class classification in a cascade of binary separations. Specifically, instead of separating multiple action classes simultaneously, the overall task is automatically divided into easier binary sub-tasks. Experiments have been carried out using challenging public datasets; the obtained results demonstrate that with identical action representation, the cascaded classifier significantly outperforms standard multi-class classifiers

A Study of Actor and Action Semantic Retention in Video Supervoxel Segmentation

Existing methods in the semantic computer vision community seem unable to deal with the explosion and richness of modern, open-source and social video content. Although sophisticated methods such as object detection or bag-of-words models have been well studied, they typically operate on low level features and ultimately suffer from either scalability issues or a lack of semantic meaning. On the other hand, video supervoxel segmentation has recently been established and applied to large scale data processing, which potentially serves as an intermediate representation to high level video semantic extraction. The supervoxels are rich decompositions of the video content: they capture object shape and motion well. However, it is not yet known if the supervoxel segmentation retains the semantics of the underlying video content. In this paper, we conduct a systematic study of how well the actor and action semantics are retained in video supervoxel segmentation. Our study has human observers watching supervoxel segmentation videos and trying to discriminate both actor (human or animal) and action (one of eight everyday actions). We gather and analyze a large set of 640 human perceptions over 96 videos in 3 different supervoxel scales. Furthermore, we conduct machine recognition experiments on a feature defined on supervoxel segmentation, called supervoxel shape context, which is inspired by the higher order processes in human perception. Our ultimate findings suggest that a significant amount of semantics have been well retained in the video supervoxel segmentation and can be used for further video analysis.Comment: This article is in review at the International Journal of Semantic Computin

Discriminatively Trained Latent Ordinal Model for Video Classification

We study the problem of video classification for facial analysis and human action recognition. We propose a novel weakly supervised learning method that models the video as a sequence of automatically mined, discriminative sub-events (eg. onset and offset phase for "smile", running and jumping for "highjump"). The proposed model is inspired by the recent works on Multiple Instance Learning and latent SVM/HCRF -- it extends such frameworks to model the ordinal aspect in the videos, approximately. We obtain consistent improvements over relevant competitive baselines on four challenging and publicly available video based facial analysis datasets for prediction of expression, clinical pain and intent in dyadic conversations and on three challenging human action datasets. We also validate the method with qualitative results and show that they largely support the intuitions behind the method.Comment: Paper accepted in IEEE TPAMI. arXiv admin note: substantial text overlap with arXiv:1604.0150

Descriptive temporal template features for visual motion recognition

In this paper, a human action recognition system is proposed. The system is based on new, descriptive `temporal template' features in order to achieve high-speed recognition in real-time, embedded applications. The limitations of the well known `Motion History Image' (MHI) temporal template are addressed and a new `Motion History Histogram' (MHH) feature is proposed to capture more motion information in the video. MHH not only provides rich motion information, but also remains computationally inexpensive. To further improve classification performance, we combine both MHI and MHH into a low dimensional feature vector which is processed by a support vector machine (SVM). Experimental results show that our new representation can achieve a significant improvement in the performance of human action recognition over existing comparable methods, which use 2D temporal template based representations