Understanding pedestrian flow patterns in urban areas could support the decision-making for infrastructure planning. By incorporating computer vision techniques into surveillance video processing, human walking trajectories in a wide area could be identified by pedestrian re-identification (ReID) across multiple cameras. Recent ReID methods mostly use convolutional neural networks equipped with deep learning techniques to extract discriminative human features from images for identity matching. However, they still suffer from realistic challenges such as occlusion and appearance variation. This paper develops a ReID-based framework for pedestrian trajectory recognition across multiple cameras. Specifically, a generic approach of explainable model design is presented, which intuitively analyzes existing baseline models based on feature visualization. Hence, a new model named OSNet + BDB is developed that extracts discriminative-and-distributed features. Additionally, an incremental feature aggregation strategy is designed for more robust identity matching. Our ReID method notably outperforms its baselines by 4% identification F1 accuracy in public benchmarks. Practically, pedestrian flow statistics in a real building are extracted for behavioral modeling. Simulations of several what-if layouts are then conducted for facility performance evaluation.

中文翻译：

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用于行人重新识别和轨迹建模的丰富和判别卷积神经网络特征

了解城市地区的行人流动模式可以支持基础设施规划的决策。通过将计算机视觉技术整合到监控视频处理中，可以通过跨多个摄像头的行人重新识别 (ReID) 来识别大范围内的人类行走轨迹。最近的 ReID 方法主要使用配备深度学习技术的卷积神经网络从图像中提取有区别的人类特征以进行身份​​匹配。然而，它们仍然面临着诸如遮挡和外观变化等现实挑战。本文开发了一个基于 ReID 的框架，用于跨多个摄像头的行人轨迹识别。具体来说，提出了一种可解释模型设计的通用方法，该方法直观地分析了基于特征可视化的现有基线模型。因此，开发了一种名为 OSNet + BDB 的新模型，用于提取判别和分布式特征。此外，增量特征聚合策略旨在实现更强大的身份匹配。我们的 ReID 方法在公共基准测试中的识别 F1 准确率明显优于其基线 4%。实际上，提取真实建筑物中的行人流量统计数据用于行为建模。然后对几种假设布局进行模拟以进行设施性能评估。提取真实建筑物中的行人流量统计数据用于行为建模。然后对几种假设布局进行模拟以进行设施性能评估。提取真实建筑物中的行人流量统计数据用于行为建模。然后对几种假设布局进行模拟以进行设施性能评估。