In video-based action recognition, viewpoint variations often pose major challenges because the same actions can appear different from different views. We use the complementary RGB and Depth information from the RGB-D cameras to address this problem. The proposed technique capitalizes on the spatio-temporal information available in the two data streams to the extract action features that are largely insensitive to the viewpoint variations. We use the RGB data to compute dense trajectories that are translated to viewpoint insensitive deep features under a non-linear knowledge transfer model. Similarly, the Depth stream is used to extract CNN-based view invariant features on which Fourier Temporal Pyramid is computed to incorporate the temporal information. The heterogeneous features from the two streams are combined and used as a dictionary to predict the label of the test samples. To that end, we propose a sparse-dense collaborative representation classification scheme that strikes a balance between the discriminative abilities of the dense and the sparse representations of the samples over the extracted heterogeneous dictionary.
In this paper, we focus on heterogeneous feature learning for RGB-D activity recognition. Considering that features from different channels could share some similar hidden structures, we propose a joint learning model to simultaneously explore the shared and feature-specific components as an instance of heterogenous multi-task learning. The proposed model in an unified framework is capable of: 1) jointly mining a set of subspaces with the same dimensionality to enable the multi-task classifier learning, and 2) meanwhile, quantifying the shared and feature-specific components of features in the subspaces. To efficiently train the joint model, a three-step iterative optimization algorithm is proposed, followed by two inference models. Extensive results on three activity datasets have demonstrated the efficacy of the proposed method. In addition, a novel RGB-D activity dataset focusing on human-object interaction is collected for evaluating the proposed method, which will be made available to the community for RGB-D activity benchmarking and analysis. Since the Microsoft Kinect has been released, the usage of marker-less body pose estimation has been enormously eased. Based on 3D skelet al pose information, complex human gestures and actions can be recognised in real time. However, due to errors in tracking or occlusions, the obtained information can be noisy. Since the RGB-D data is available, the 3D or 2D shape of the person can be used instead. However, depending on the viewpoint and the action to recognise, it might present a low discriminative value. In this paper, the combination of body pose estimation and 2D shape, in order to provide additional characteristic value, is considered so as to improve human action recognition. Using efficient feature extraction techniques, skelet al and silhouette-based features are obtained which are low dimensional and can be obtained in real time. These two features are then combined by means of feature fusion. The proposed approach is validated using a state-of-the-art learning method and the MSR Action3D dataset as benchmark. The obtained results show that the fused feature achieves to improve the recognition rates, outperforming state-of-the-art results in recognition rate and robustness. Recently, the low-cost Microsoft Kinect sensor, which can capture real-time high-resolution RGB and depth visual information, has attracted increasing attentions for a wide range of applications in computer vision. Existing techniques extract hand-tuned features from the RGB and the depth data separately and heuristically fuse them, which would not fully exploit the complementarity of both data sources. In this paper, we introduce an adaptive learning methodology to automatically extract (holistic) spatio-temporal features, simultaneously fusing the RGB and depth information, from RGB-D video data for visual recognition tasks. We address this as an optimization problem using our proposed restricted graph-based genetic programming (RGGP) approach, in which a group of primitive 3D operators are first randomly assembled as graph-based combinations and then evolved generation by generation by evaluating on a set of RGB-D video samples. Finally the best-performed combination is selected as the (near-)optimal representation for a pre-defined task. The proposed method is systematically evaluated on a new hand gesture dataset, SKIG, that we collected ourselves and the public MSR Daily Activity 3D dataset, respectively. Extensive experimental results show that our approach leads to significant advantages compared with state-of-the-art hand-crafted and machine-learned features. Recognizing the events and objects in the video sequence are two challenging tasks due to the complex temporal structures and the large appearance variations. In this paper, we propose a 4D human-object interaction model, where the two tasks jointly boost each other. Our human-object interaction is defined in 4D space: i) the co occurrence and geometric constraints of human pose and object in 3D space, ii) the sub-events transition and objects coherence in 1D temporal dimension. We represent the structure of events, sub-events and objects in a hierarchical graph. For an input RGB-depth video, we design a dynamic programming beam search algorithm to: i) segment the video, ii) recognize the events, and iii) detect the objects simultaneously. For evaluation, we built a large-scale multiview 3D event dataset which contains 3815 video sequences and 383,036 RGBD frames captured by the Kinect cameras. The experiment results on this dataset show the effectiveness of our method. This paper proposes a novel approach to action recognition from RGB-D cameras, in which depth features and RGB visual features are jointly used. Rich heterogeneous RGB and depth data are effectively compressed and projected to a learned shared space, in order to reduce noise and capture useful information for recognition. Knowledge from various sources can then be shared with others in the learned space to learn cross-modal features. This guides the discovery of valuable information for recognition. To capture complex spatiotemporal structural relationships in visual and depth features, we represent both RGB and depth data in a matrix form. We formulate the recognition task as a low-rank bilinear model composed of row and column parameter matrices. The rank of the model parameter is minimized to build a low-rank classifier, which is beneficial for improving the generalization power. The proposed method is extensively evaluated on two public RGB-D action datasets, and achieves state-of-the-art results. It also shows promising results if RGB or depth data are missing in training or testing procedure. We consider the problem of detecting past activities as well as anticipating which activity will happen in the future and how. We start by modeling the rich spatio-temporal relations between human poses and objects (called affordances) using a conditional random field (CRF). However, because of the ambiguity in the temporal segmentation of the sub-activities that constitute an activity, in the past as well as in the future, multiple graph structures are possible. In this paper, we reason about these alternate possibilities by reasoning over multiple possible graph structures. We obtain them by approximating the graph with only additive features, which lends to efficient dynamic programming. Starting with this proposal graph structure, we then design moves to obtain several other likely graph structures. We then show that our approach improves the state-of-the-art significantly for detecting past activities as well as for anticipating future activities, on a dataset of 120 activity videos collected from four subjects. Microsoft Kinect's output is a multi-modal signal which gives RGB videos, depth sequences and skeleton information simultaneously. Various action recognition techniques focused on different single modalities of the signals and built their classifiers over the features extracted from one of these channels. For better recognition performance, it's desirable to fuse these multi-modal information into an integrated set of discriminative features. Most of current fusion methods merged heterogeneous features in a holistic manner and ignored the complementary properties of these modalities in finer levels. In this paper, we proposed a new hierarchical bag-of-words feature fusion technique based on multi-view structured spar-sity learning to fuse atomic features from RGB and skeletons for the task of action recognition.