用视觉节奏筛选关键帧，结合YOLO实现高效车辆计数
Combining YOLO and Visual Rhythm for Vehicle Counting
- 通过视觉节奏生成时空图像，仅在关键帧检测车辆
- 平均计数准确率达99.15%，处理速度是传统方法3倍
- 适用于单向移动目标检测，部署简单效率高
基于视频的车辆检测与计数在交通基础设施管理中至关重要。传统图像计数方法通常分为检测和跟踪两步，需对所有视频帧进行处理，导致计算开销大。本文提出一种更高效的方法，无需跟踪步骤，仅在关键帧上检测车辆。该方法结合YOLO进行车辆检测，利用视觉节奏（Visual Rhythm）生成时间-空间图像，识别含有效信息的帧。实验基于真实视频数据，在多个场景下平均计数准确率达99.15%，处理速度比基于跟踪的方法快三倍。该方法还可推广至其他单向运动目标的检测与识别任务。
原文摘要 · Abstract (English)
Video-based vehicle detection and counting play a critical role in managing transport infrastructure. Traditional image-based counting methods usually involve two main steps: initial detection and subsequent tracking, which are applied to all video frames, leading to a significant increase in computational complexity. To address this issue, this work presents an alternative and more efficient method for vehicle detection and counting. The proposed approach eliminates the need for a tracking step and focuses solely on detecting vehicles in key video frames, thereby increasing its efficiency. To achieve this, we developed a system that combines YOLO, for vehicle detection, with Visual Rhythm, a way to create time-spatial images that allows us to focus on frames that contain useful information. Additionally, this method can be used for counting in any application involving unidirectional moving targets to be detected and identified. Experimental analysis using real videos shows that the proposed method achieves mean counting accuracy around 99.15% over a set of videos, with a processing speed three times faster than tracking based approaches.
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