Heterogeneous Features Fusion with Collaborative Representation Learning for 3D Action Recognition

This paper proposes an action recognition framework for depth map sequences using the 3D Space-Time Auto-Correlation of Gradients (STACOG) algorithm. First, each depth map sequence is split into two sets of sub-sequences of two different frame lengths individually. Second, a number of Depth Motion Maps (DMMs) sequences from every set are generated and are fed into STACOG to find an auto-correlation feature vector. For two distinct sets of sub-sequences, two auto-correlation feature vectors are obtained and applied gradually to L2-regularized Collaborative Representation Classifier (L2-CRC) for computing a pair of sets of residual values. Next, the Logarithmic Opinion Pool (LOGP) rule is used to combine the two different outcomes of L2-CRC and to allocate an action label of the depth map sequence. Finally, our proposed framework is evaluated on three benchmark datasets named MSR-action 3D dataset, DHA dataset, and UTD-MHAD dataset. We compare the experimental results of our proposed framework with state-of-the-art approaches to prove the effectiveness of the proposed framework. The computational efficiency of the framework is also analyzed for all the datasets to check whether it is suitable for real-time operation or not.

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Effective Collaborative Representation Learning for Multilabel Text Categorization

IEEE Transactions on Neural Networks and Learning Systems ◽

10.1109/tnnls.2021.3069647 ◽

2021 ◽

pp. 1-15

Author(s):

Hao Wu ◽

Shaowei Qin ◽

Rencan Nie ◽

Jinde Cao ◽

Sergey Gorbachev

Keyword(s):

Text Categorization ◽

Representation Learning ◽

Collaborative Representation

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Improved Collaborative Representation Classifier Based on l2-Regularized for Human Action Recognition

Journal of Electrical and Computer Engineering ◽

10.1155/2017/8191537 ◽

2017 ◽

Vol 2017 ◽

pp. 1-6

Author(s):

Shirui Huo ◽

Tianrui Hu ◽

Ce Li

Keyword(s):

Action Recognition ◽

Human Action Recognition ◽

Test Sample ◽

Human Action ◽

Superior Performance ◽

Depth Image ◽

Collaborative Representation ◽

Depth Images ◽

Spatiotemporal Information ◽

Depth Motion Maps

Human action recognition is an important recent challenging task. Projecting depth images onto three depth motion maps (DMMs) and extracting deep convolutional neural network (DCNN) features are discriminant descriptor features to characterize the spatiotemporal information of a specific action from a sequence of depth images. In this paper, a unified improved collaborative representation framework is proposed in which the probability that a test sample belongs to the collaborative subspace of all classes can be well defined and calculated. The improved collaborative representation classifier (ICRC) based on l2-regularized for human action recognition is presented to maximize the likelihood that a test sample belongs to each class, then theoretical investigation into ICRC shows that it obtains a final classification by computing the likelihood for each class. Coupled with the DMMs and DCNN features, experiments on depth image-based action recognition, including MSRAction3D and MSRGesture3D datasets, demonstrate that the proposed approach successfully using a distance-based representation classifier achieves superior performance over the state-of-the-art methods, including SRC, CRC, and SVM.

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