Spatial-temporal pyramid based Convolutional Neural Network for action recognition

Zheng, Zhenxing; An, Gaoyun; Wu, Dapeng; Ruan, Qiuqi

doi:10.1016/j.neucom.2019.05.058

Cited by 51 publications

(16 citation statements)

References 17 publications

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“…Extracting local features from the shape can be done by solving a Poisson equation and finally using a template-based nearest neighbor classifier for classification. Xu et al and Zheng et al [15,16] tracked the main joints of the human body, used a parameterized method to represent the rotation and translation of each part of the human body, and used these parameters to express the actions. Poultangari et al [17] introduced cloud theory and particle swarm algorithm and used particle swarm optimization backpropagation neural network (PSO-BPNN) method for structural damage identification.…”

Section: Related Workmentioning

confidence: 99%

Sports Action Recognition Based on Particle Swarm Optimization Neural Networks

Zhang

2022

Wireless Communications and Mobile Computing

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Video acquisition has become more convenient as science and technology have progressed, and the development of mobile Internet has resulted in a large amount of video data being generated every day. The question of how to analyze these videos automatically has become urgent. Among them, the study of sports movement recognition in video has important theoretical implications in sports research as well as practical application value. This paper proposes a PSO-NN-based sports action recognition model. Kernel principal component analysis is used to extract and analyze the characteristics of sports movements. The improved neural network is used to identify common human postures in sports, and the classification and block background estimation method is used to detect human targets. The feature extraction of targets is completed according to the edge features, and the feature extraction of targets is completed according to the edge features. Finally, the feature vectors are trained using a backpropagation neural network (BPNN), and the parameters of the BPNN are chosen using the PSO algorithm to create a classifier for sports action recognition. The results show that this model improves the accuracy of sports video recognition and is an effective method of sports action recognition when compared to the comparison model.

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Section: Related Workmentioning

confidence: 99%

Sports Action Recognition Based on Particle Swarm Optimization Neural Networks

Zhang

2022

Wireless Communications and Mobile Computing

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“…[28] uses the relative position and relative speed to provide spatial information and dynamic information in skeletal action sequences, and build a bidirectional LSTM-CNN to fuse spatial-temporal information for skeleton-based action recognition. Spatial-Temporal Pyramid Network [29] (S-TPNet) extracts multiscale appearance features from different stages of the 2D CNN. It groups the frame features into different snippet-level features and uses fully-connected layers to reason about snippet relations.…”

Section: Relation Modelmentioning

confidence: 99%

Efficient Spatialtemporal Context Modeling for Action Recognition

Cao,

Lu,

Zhang

et al. 2021

Preprint

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Contextual information plays an important role in action recognition. Local operations have difficulty to model the relation between two elements with a long-distance interval. However, directly modeling the contextual information between any two points brings huge cost in computation and memory, especially for action recognition, where there is an additional temporal dimension. Inspired from 2D criss-cross attention used in segmentation task, we propose a recurrent 3D criss-cross attention (RCCA-3D) module to model the dense long-range spatiotemporal contextual information in video for action recognition. The global context is factorized into sparse relation maps. We model the relationship between points in the same line along the direction of horizon, vertical and depth at each time, which forms a 3D criss-cross structure, and duplicate the same operation with recurrent mechanism to transmit the relation between points in a line to a plane finally to the whole spatiotemporal space. Compared with the non-local method, the proposed RCCA-3D module reduces the number of parameters and FLOPs by 25% and 30% for video context modeling. We evaluate the performance of RCCA-3D with two latest action recognition networks on three datasets and make a thorough analysis of the architecture, obtaining the optimal way to factorize and fuse the relation maps. Comparisons with other state-of-the-art methods demonstrate the effectiveness and efficiency of our model.

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“…Ke et al propose Frame Segmentation Network, improving mAP (mean average precision) and IoU (Intersection over Union) simultaneously [20]. As for action recognition, the S-TPNet proposed in [21] has a good performance. Its accuracy is around 74% on dataset HMDB51, and around 95% on dataset UCF101.…”

Section: Related Workmentioning

confidence: 99%

CIES: Cloud-based Intelligent Evaluation Service for video homework using CNN-LSTM network

et al. 2020

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Video (used as a form of examination or homework) as an efficient approach for examining students' abilities is drawing increasing attention in the education field. How to assess video assignments effectively and accurately has become a significant topic in academia. This work proposes a method based on a multi-channel CNN-LSTM hybrid architecture to extract and classify image features such as students' actions and expressions, as well as audio features such as speech rates and pauses in the video assignments, and then conducts a two-category assessment of "qualified" or "unqualified". Additionally, build this system in a cloud computing environment as a Cloud-based Intelligent Evaluation Service application could provide universal service to meet the needs of multiple teaching units. The proposed method is shown to be feasible and effective through experiments.

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Spatial-temporal pyramid based Convolutional Neural Network for action recognition

Cited by 51 publications

References 17 publications

Sports Action Recognition Based on Particle Swarm Optimization Neural Networks

Sports Action Recognition Based on Particle Swarm Optimization Neural Networks

Efficient Spatialtemporal Context Modeling for Action Recognition

CIES: Cloud-based Intelligent Evaluation Service for video homework using CNN-LSTM network

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