融合时空分析的实时视频异常检测新架构。
Hybrid Architecture for Real-Time Video Anomaly Detection: Integrating Spatial and Temporal Analysis
- 分时空双路处理：用YOLOv7分析图像，用VGG19+GRU处理序列。
- 并行与串行两种结构对比，提升异常识别准确率。
- 适合智能监控、工业安全等实时场景应用。
本文提出一种新型实时视频异常检测架构，灵感源自人类对空间与时间信息的综合判断。该方法采用双模型设计：时间分析部分使用VGG19结合GRU的卷积循环网络处理视频序列；空间分析部分则采用YOLOv7对单帧图像进行目标检测。两种分析可并行执行，最终融合结果做出预测，也可串行进行，即先由空间分析增强输入数据再送入时序模型。通过实验对比两种结构，验证了该混合架构在视频异常检测任务中的有效性。
原文摘要 · Abstract (English)
In this paper, we propose a new architecture for real-time anomaly detection in video data, inspired by human behavior combining spatial and temporal analyses. This approach uses two distinct models: (i) for temporal analysis, a recurrent convolutional network (CNN + RNN) is employed, associating VGG19 and a GRU to process video sequences; (ii) regarding spatial analysis, it is performed using YOLOv7 to analyze individual images. These two analyses can be carried out either in parallel, with a final prediction that combines the results of both analysis, or in series, where the spatial analysis enriches the data before the temporal analysis. Some experimentations are been made to compare these two architectural configurations with each other, and evaluate the effectiveness of our hybrid approach in video anomaly detection.
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