用隐式一对多匹配提升视频行人计数精度
Video Individual Counting With Implicit One-to-Many Matching
- 采用隐式一对多匹配，利用行人社交聚集行为
- 在SenseCrowd和CroHD上达到当前最佳性能
- 适合需要精准追踪多人流动的场景应用
视频个体计数（VIC）是一项新兴任务，旨在从视频中估计行人流量，超越传统视频人群计数（VCC）仅统计每帧行人数量的局限。与仅学习跨帧重复模式的VCC不同，VIC的核心挑战在于如何识别帧间共存的行人，本质上是一个对应关系问题。现有方法多采用严格的一对一（O2O）匹配策略，对外观变化或漏检敏感。本文提出将O2O放宽为一对多（O2M）匹配，更契合VIC本质，并利用行人行走时的社交聚集特性。为此，我们设计了OMAN模型，包含隐式上下文生成器与一对多配对器。在SenseCrowd和CroHD基准上的实验表明，OMAN达到当前最优性能。代码已开源。
原文摘要 · Abstract (English)
Video Individual Counting (VIC) is a recently introduced task that aims to estimate pedestrian flux from a video. It extends conventional Video Crowd Counting (VCC) beyond the per-frame pedestrian count. In contrast to VCC that only learns to count repeated pedestrian patterns across frames, the key problem of VIC is how to identify co-existent pedestrians between frames, which turns out to be a correspondence problem. Existing VIC approaches, however, mainly follow a one-to-one (O2O) matching strategy where the same pedestrian must be exactly matched between frames, leading to sensitivity to appearance variations or missing detections. In this work, we show that the O2O matching could be relaxed to a one-to-many (O2M) matching problem, which better fits the problem nature of VIC and can leverage the social grouping behavior of walking pedestrians. We therefore introduce OMAN, a simple but effective VIC model with implicit One-to-Many mAtchiNg, featuring an implicit context generator and a one-to-many pairwise matcher. Experiments on the SenseCrowd and CroHD benchmarks show that OMAN achieves the state-of-the-art performance. Code is available at \href{https://github.com/tiny-smart/OMAN}{OMAN}.
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