用人体车体检测优化监控录像，节省三分之二存储空间
Intelligent Video Recording Optimization using Activity Detection for Surveillance Systems
- 结合帧差法与YOLOv9检测运动中的行人和车辆
- 行人检测精度0.884，车辆0.855，存储量减少三分之二
- 适合需要高效存档的智能安防场景
监控系统常面临海量视频数据中大量无关内容的问题，导致存储效率低下且事件检索困难。本文提出一种基于活动检测的优化录像方案，采用帧差法结合YOLOv9进行物体检测，仅记录包含人或车辆活动的场景，有效减少冗余数据。实验表明，该方法在行人检测上达到0.884的精度，在车辆检测上达0.855，相比仅依赖运动检测的传统系统，存储需求降低三分之二。尽管如此，在强风等恶劣天气下仍存在误检与漏检问题。
原文摘要 · Abstract (English)
Surveillance systems often struggle with managing vast amounts of footage, much of which is irrelevant, leading to inefficient storage and challenges in event retrieval. This paper addresses these issues by proposing an optimized video recording solution focused on activity detection. The proposed approach utilizes a hybrid method that combines motion detection via frame subtraction with object detection using YOLOv9. This strategy specifically targets the recording of scenes involving human or car activity, thereby reducing unnecessary footage and optimizing storage usage. The developed model demonstrates superior performance, achieving precision metrics of 0.855 for car detection and 0.884 for person detection, and reducing the storage requirements by two-thirds compared to traditional surveillance systems that rely solely on motion detection. This significant reduction in storage highlights the effectiveness of the proposed approach in enhancing surveillance system efficiency. Nonetheless, some limitations persist, particularly the occurrence of false positives and false negatives in adverse weather conditions, such as strong winds.
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