scholarly journals Multi-Modal Human Action Recognition With Sub-Action Exploiting and Class-Privacy Preserved Collaborative Representation Learning

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 39920-39933
Author(s):  
Chengwu Liang ◽  
Deyin Liu ◽  
Lin Qi ◽  
Ling Guan
Keyword(s):  
Action Recognition ◽  
Human Action Recognition ◽  
Representation Learning ◽  
Human Action ◽  
Collaborative Representation

scholarly journals Exploring 3D Human Action Recognition Using STACOG on Multi-View Depth Motion Maps Sequences

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3642
Author(s):  
Mohammad Farhad Bulbul ◽  
Sadiya Tabussum ◽  
Hazrat Ali ◽  
Wenli Zheng ◽  
Mi Young Lee ◽  
...  
Keyword(s):  
Action Recognition ◽  
Depth Map ◽  
Human Action Recognition ◽  
Human Action ◽  
Collaborative Representation ◽  
Auto Correlation ◽  
Time Operation ◽  
Real Time Operation ◽  
Benchmark Datasets ◽  
Depth Motion Maps

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.

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

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.

A Flexible High-Level Fusion for an Accurate Human Action Recognition System

2020 ◽  
Vol 29 (12) ◽  
pp. 2050190
Author(s):  
Amel Ben Mahjoub ◽  
Mohamed Atri
Keyword(s):  
Action Recognition ◽  
Short Term Memory ◽  
Recognition Rate ◽  
Human Action Recognition ◽  
Local Binary Patterns ◽  
Low Complexity ◽  
Recognition System ◽  
Human Action ◽  
Collaborative Representation ◽  
Depth Video

Action recognition is a very effective method of computer vision areas. In the last few years, there has been a growing interest in Deep learning networks as the Long Short–Term Memory (LSTM) architectures due to their efficiency in long-term time sequence processing. In the light of these recent events in deep neural networks, there is now considerable concern about the development of an accurate action recognition approach with low complexity. This paper aims to introduce a method for learning depth activity videos based on the LSTM and the classification fusion. The first step consists in extracting compact depth video features. We start with the calculation of Depth Motion Maps (DMM) from each sequence. Then we encode and concatenate contour and texture DMM characteristics using the histogram-of-oriented-gradient and local-binary-patterns descriptors. The second step is the depth video classification based on the naive Bayes fusion approach. Training three classifiers, which are the collaborative representation classifier, the kernel-based extreme learning machine and the LSTM, is done separately to get classification scores. Finally, we fuse the classification score outputs of all classifiers with the naive Bayesian method to get a final predicted label. Our proposed method achieves a significant improvement in the recognition rate compared to previous work that has used Kinect v2 and UTD-MHAD human action datasets.

Human action recognition using simple geometric features and a finite state machine

2013 ◽  
Vol 18 (2-3) ◽  
pp. 49-60 ◽  
Author(s):  
Damian Dudzńiski ◽  
Tomasz Kryjak ◽  
Zbigniew Mikrut
Keyword(s):  
Action Recognition ◽  
Finite State Machine ◽  
Recognition Rate ◽  
Human Action Recognition ◽  
Human Action ◽  
Video Stream ◽  
State Machine ◽  
Recognition Algorithm ◽  
Finite State ◽  
Correct Recognition Rate

Abstract In this paper a human action recognition algorithm, which uses background generation with shadow elimination, silhouette description based on simple geometrical features and a finite state machine for recognizing particular actions is described. The performed tests indicate that this approach obtains a 81 % correct recognition rate allowing real-time image processing of a 360 X 288 video stream.

Deep Learning for Human Action Recognition Survey

2018 ◽  
Vol 6 (10) ◽  
pp. 323-328
Author(s):  
K.Kiruba . ◽  
D. Shiloah Elizabeth ◽  
C Sunil Retmin Raj
Keyword(s):  
Deep Learning ◽  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action
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