Research on Human Action Feature Detection and Recognition Algorithm Based on Deep Learning

Wu, Zhenfeng; Hua-ying, DU

doi:10.1155/2022/4652946

Cited by 2 publications

(1 citation statement)

References 34 publications

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“…Using different modalities increases the accuracy of human activity recognition (HAR) systems. Momin et al [50], Sun et al [51], and Wu and Du [52] investigated HAR systems based on different data modalities. Ahn et al [53], Vaitesswar and Yeo [54], and Lee et al [55] used skeleton data for HAR.…”

Section: State Of the Artmentioning

confidence: 99%

Deep Learning Approach for Human Action Recognition Using a Time Saliency Map Based on Motion Features Considering Camera Movement and Shot in Video Image Sequences

Alavigharahbagh,

Hajihashemi,

Machado

et al. 2023

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In this article, a hierarchical method for action recognition based on temporal and spatial features is proposed. In current HAR methods, camera movement, sensor movement, sudden scene changes, and scene movement can increase motion feature errors and decrease accuracy. Another important aspect to take into account in a HAR method is the required computational cost. The proposed method provides a preprocessing step to address these challenges. As a preprocessing step, the method uses optical flow to detect camera movements and shots in input video image sequences. In the temporal processing block, the optical flow technique is combined with the absolute value of frame differences to obtain a time saliency map. The detection of shots, cancellation of camera movement, and the building of a time saliency map minimise movement detection errors. The time saliency map is then passed to the spatial processing block to segment the moving persons and/or objects in the scene. Because the search region for spatial processing is limited based on the temporal processing results, the computations in the spatial domain are drastically reduced. In the spatial processing block, the scene foreground is extracted in three steps: silhouette extraction, active contour segmentation, and colour segmentation. Key points are selected at the borders of the segmented foreground. The last used features are the intensity and angle of the optical flow of detected key points. Using key point features for action detection reduces the computational cost of the classification step and the required training time. Finally, the features are submitted to a Recurrent Neural Network (RNN) to recognise the involved action. The proposed method was tested using four well-known action datasets: KTH, Weizmann, HMDB51, and UCF101 datasets and its efficiency was evaluated. Since the proposed approach segments salient objects based on motion, edges, and colour features, it can be added as a preprocessing step to most current HAR systems to improve performance.

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Section: State Of the Artmentioning

confidence: 99%

Deep Learning Approach for Human Action Recognition Using a Time Saliency Map Based on Motion Features Considering Camera Movement and Shot in Video Image Sequences

Alavigharahbagh,

Hajihashemi,

Machado

et al. 2023

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Deep Learning based Object Detection Techniques in Videos

Kusuma

Kiran

Kulkarni

2022

AIST

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Deep learning technology is often used for object detection. It has received attention recently because to the intimate connections between object detection, video analysis, and picture understanding. The goal of object detection has been pursued using a variety of models, and this is immensely beneficial to humanity. The most recent technical developments have helped the computational experiments, which would not have been conceivable if they had been tried using the conventional techniques. The powerful approaches employed in deep learning can show noticeably higher efficiency when compared to conventional designs and architectures. Numerous strategies and techniques have been used in deep learning to boost accuracy, and their drawbacks have also been somewhat addressed in order to lessen them. This study's main objective is to give an overview of several object detection procedures and approaches based on deep learning. Additionally, it lists the benefits and drawbacks of various object identification systems based on their potential applications and limitations.

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Research on Human Action Feature Detection and Recognition Algorithm Based on Deep Learning

Cited by 2 publications

References 34 publications

Deep Learning Approach for Human Action Recognition Using a Time Saliency Map Based on Motion Features Considering Camera Movement and Shot in Video Image Sequences

Deep Learning Approach for Human Action Recognition Using a Time Saliency Map Based on Motion Features Considering Camera Movement and Shot in Video Image Sequences

Deep Learning based Object Detection Techniques in Videos

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