Understanding human activity is very challenging even with the recently developed 3D depth sensors. To solve this problem, this work investigates a novel deep structured model, which adaptively decomposes an activity instance into temporal parts using the convolutional neural networks. Our model advances the traditional deep learning approaches in two aspects. First, we incorporate latent temporal structure into the deep model, accounting for large temporal variations of diverse human activities. In particular, we utilize the latent variables to decompose the input activity into a number of temporally segmented sub-activities, and accordingly feed them into the parts (i.e. sub-networks) of the deep architecture. Second, we incorporate a radius---margin bound as a regularization term into our deep model, which effectively improves the generalization performance for classification. For model training, we propose a principled learning algorithm that iteratively (i) discovers the optimal latent variables (i.e. the ways of activity decomposition) for all training instances, (ii) updates the classifiers based on the generated features, and (iii) updates the parameters of multi-layer neural networks. In the experiments, our approach is validated on several complex scenarios for human activity recognition and demonstrates superior performances over other state-of-the-art approaches.
In the mean time, the past few years have seen a resurgence of research in the design of deep neutral networks, and impressive progresses have been made on learning image features from raw data . To address human action recognition from videos, developed a novel deep architecture of convolutional networks, where they extracted features from both spatial and temporal dimensions. @cite_1 proposed to incorporate a new Switchable Restricted Boltzmann Machine (SRBM) to explicitly model the complex mixture of visual appearance for pedestrian detection, and train their model using an EM-type interative algorithm. Amer and Todorovic applied Sum Product Networks (SPNs) to model human activities based on variable primitive actions. Our deep model is partially motivated by these works, and we target on an more flexible and powerful solution by jointly considering the latent structure embedding, feature learning, and radius-margin classification.
@cite_1
2084997728
In this paper, we propose a Switchable Deep Network (SDN) for pedestrian detection. The SDN automatically learns hierarchical features, salience maps, and mixture representations of different body parts. Pedestrian detection faces the challenges of background clutter and large variations of pedestrian appearance due to pose and viewpoint changes and other factors. One of our key contributions is to propose a Switchable Restricted Boltzmann Machine (SRBM) to explicitly model the complex mixture of visual variations at multiple levels. At the feature levels, it automatically estimates saliency maps for each test sample in order to separate background clutters from discriminative regions for pedestrian detection. At the part and body levels, it is able to infer the most appropriate template for the mixture models of each part and the whole body. We have devised a new generative algorithm to effectively pretrain the SDN and then fine-tune it with back-propagation. Our approach is evaluated on the Caltech and ETH datasets and achieves the state-of-the-art detection performance.