Research on Crowd Abnormal Behavior Detection Based on Improved SSD
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摘要: 针对人群异常行为检测任务中存在的算法复杂度较高，重叠遮挡等带来的检测精度低等问题，本文提出一种基于改进SSD（Single Shot Multi-box Detector）的人群异常行为检测算法。首先采用轻量级网络MobileNet v2代替原始特征提取网络VGG-16，并通过可变形卷积模块构建卷积层来增强感受野，然后通过将位置信息整合到通道注意力中来进行特征增强，能够捕获空间位置之间的远程依赖关系，从而可以较好处理重叠遮挡问题。实验结果表明，本文提出的算法对人群异常行为具有较好的检测效果。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.
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图 1 基于改进SSD的人群异常行为检测模型
Figure 1. Crowd abnormal behavior detection model based on improved SSD
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图 7 本文模型在Ped1上的训练损失曲线
Figure 7. The training loss curve of the model in this paper on Ped1
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图 8 本文模型在Ped2上的训练损失曲线
Figure 8. The training loss curve of the model in this paper on Ped2
表 1 基于改进SSD的消融实验
Table 1 Ablation experiment based on improved SSD
Methods Ped1 Ped2 Test speed /fps AUC/% Test speed /fps AUC/% VGG-16 21.96 61.98 20.36 63.81 MobileNet v2 27.92 65.26 27.02 70.25 MobileNet v2+Deformable Conv 26.68 69.81 25.73 77.98 MobileNet v2+Deformable Conv+CA 26.59 74.50 25.41 88.93 
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表 2 不同检测模型性能对比分析
Table 2 Comparative analysis of the performance of different detection models
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