3D human action analysis and recognition through GLAC descriptor on 2D motion and static posture images

Bulbul, Mohammad Farhad; Islam, Saiful; Ali, Hazrat

doi:10.1007/s11042-019-7365-2

Cited by 28 publications

(14 citation statements)

References 65 publications

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“…They used a Sequential Extreme Learning Machine classifier. To improve results, [ 36 ] used two types of images that are obtained by using the 3D Motion Trail Model (3DMTM). In their method feature vectors are mined from MHIs and SHIs by the GLAC feature descriptor.…”

Section: Related Workmentioning

confidence: 99%

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

Bulbul¹,

Tabussum²,

Ali

et al. 2021

Sensors

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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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Section: Related Workmentioning

confidence: 99%

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

Bulbul¹,

Tabussum²,

Ali

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…Nowadays, with the widespread of the Artificial Intelligence (AI) in various fields, surveillance system has been born, which gradually expands the advantage of deep learning in the field of visual computers and clarifies the development direction of image processing technology [1][2][3]. After deep learning is optimized, related algorithms and models have been applied to identify, track, and detect human body's motion postures, and excellent results have been achieved [4,5].…”

Section: Introductionmentioning

confidence: 99%

“…is method analyzes the contour of the human body motion posture, uses the training characteristics of the deep learning network and the reinforcement learning network to obtain the human body motion posture change information, obtains the general direction of the human body motion posture, and realizes the design of the human body motion posture detection algorithm. e contribution of this paper is as follows: (1) the algorithm in this paper uses deep reinforcement learning to detect human motion posture, determine the position and direction of human motion posture features, and obtain human motion posture features, which can improve the accuracy of human motion posture detail feature extraction. (2) e paper proposes an antigenantibody binding method to detect human motion posture.…”

Section: Introductionmentioning

confidence: 99%

Human Motion Posture Detection Algorithm Using Deep Reinforcement Learning

Li-min

Han

2021

Mobile Information Systems

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To address problems of serious loss of details and low detection definition in the traditional human motion posture detection algorithm, a human motion posture detection algorithm using deep reinforcement learning is proposed. Firstly, the perception ability of deep learning is used to match human motion feature points to obtain human motion posture features. Secondly, normalize the human motion image, take the color histogram distribution of human motion posture as the antigen, search the region close to the motion posture in the image, and take its candidate region as the antibody. By calculating the affinity between the antigen and the antibody, the feature extraction of human motion posture is realized. Finally, using the training characteristics of deep learning network and reinforcement learning network, the change information of human motion posture is obtained, and the design of human motion posture detection algorithm is realized. The results show that when the image resolution is 384 × 256 px, the motion pose contour detection accuracy of this algorithm is 87%. When the image size is 30 MB, the recognition time of this method is only 0.8 s. When the number of iterations is 500, the capture rate of human motion posture details can reach 98.5%. This shows that the proposed algorithm can improve the definition of human motion posture contour, improve the posture detailed capture rate, reduce the loss of detail, and have better effect and performance.

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“…Bobick et al [7] proposed Motion History Images (MHIs), it included spatial characteristics, but did not completely retain the spatial information. Bulbul et al [11]extracted GLAC [12] features from Static History Images (SHIs), but SHIs still kept the temporal information on the basis of discarding spatial information. Elmadany et al [13] [14] used a new deep video feature mapping which added temporal information on the basis of DMM, called hierarchical pyramid DMM (HP-DMM).…”

Section: Introductionmentioning

confidence: 99%

Depth Sequential Information Entropy Maps and Multi-Label Subspace Learning for Human Action Recognition

et al. 2020

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Human action recognition plays a key role in human-computer interaction in complex environments. However, similar actions will lead to poor feature sequence extraction and result in a reduction in recognition accuracy. This paper proposes a method (Action-Fusion: Multi-label subspace Learning (MLSL)) from depth maps called Depth Sequential Information Entropy Maps (DSIEM) and skeleton data for human action recognition in multiple modal features. The DSIEM describe the spatial information of human motion with information entropy, and describe the temporal information through stitching. DSIEM can reduce the redundancy of depth sequences and effectively capture spatial motion states. MLSL studies the relationship between different modalities and the inherent connection between different labels. The method is evaluated on three public datasets: Microsoft action 3D dataset (MSR Action3D), University of Texas at Dallas-multimodal human action dataset (UTD-MHAD), UTD MHAD-Kinect Version-2 (UTD-MHAD-Kinect V2). Experimental results show that the proposed MLSL model obtains new state-of-the-art results, including achieving the average rate of the MSR Action3D to 93.55%, the average rate of the UTD-MHAD to 88.37% and the average rate of the UTD-MHAD-Kinect V2 to 90.66%.

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3D human action analysis and recognition through GLAC descriptor on 2D motion and static posture images

Cited by 28 publications

References 65 publications

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

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

Human Motion Posture Detection Algorithm Using Deep Reinforcement Learning

Depth Sequential Information Entropy Maps and Multi-Label Subspace Learning for Human Action Recognition

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