Research on Crowd Abnormal Behavior Detection Based on Improved SSD

KANG Jie; TIAN Ye; YANG Gang

Volume 44 Issue 12

Dec. 2022

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KANG Jie, TIAN Ye, YANG Gang. Research on Crowd Abnormal Behavior Detection Based on Improved SSD[J]. Infrared Technology , 2022, 44(12): 1316-1323.

Citation:

KANG Jie, TIAN Ye, YANG Gang. Research on Crowd Abnormal Behavior Detection Based on Improved SSD[J]. Infrared Technology , 2022, 44(12): 1316-1323.

KANG Jie, TIAN Ye, YANG Gang. Research on Crowd Abnormal Behavior Detection Based on Improved SSD[J]. Infrared Technology , 2022, 44(12): 1316-1323.

Citation:

KANG Jie, TIAN Ye, YANG Gang. Research on Crowd Abnormal Behavior Detection Based on Improved SSD[J]. Infrared Technology , 2022, 44(12): 1316-1323.

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Research on Crowd Abnormal Behavior Detection Based on Improved SSD

1.
School of Electrical and Control Engineering, Shaanxi University of Science and Technology, Xi'an 710021, China
2.
School of Communication and Information Engineering, Xi'an University of Posts and Telecommunications, Xi'an 710121, China

Received Date: 2022-04-03
Rev Recd Date: 2022-07-12
Publish Date: 2022-12-20

Abstract

Abstract

Aiming at the problems of high algorithmic complexity and low detection accuracy caused by overlapping occlusions in abnormal crowd behavior detection, this paper proposes an algorithm for crowd abnormal behavior detection based on an improved single-shot multi-box detector(SSD). First, the lightweight network MobileNet v2 was used to replace the original feature extraction network VGG-16, and a convolutional layer was constructed by a deformable convolution module to enhance the receptive field. Feature enhancement was performed by integrating the position information into the channel attention, which can capture long-range dependencies between spatial locations, allowing for better handling of overlapping occlusions. The experimental results show that the proposed algorithm has a good detection effect on abnormal crowd behavior.
- deep learning,
- abnormal behavior detection,
- SSD network,
- deformable convolution,
- attention mechanism

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References(13)

References

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