Crowd analysis has become an important topic of research for visual surveillance community. This paper proposes an active contour-based trajectory clustering approach for crowd flow segmentation. To this end, the active contour method is applied to segment the foreground crowd region with an aim to optimize further tracking. From the segmented foreground region, spatiotemporal interest points are detected and tracked to extract crowd trajectories. The trajectories are then parameterized by their shape, location information, flow direction, and neighborhood density. A clustering algorithm is designed to cluster these trajectories, and further flow patterns are segmented by merging trajectory clusters on the basis of their spatial overlapping and distinction in location and in flow direction. Once the flow patterns are segmented, trajectory density of each segment is estimated to analyze crowd flow. Experiments are conducted on three publicly available UCF Web, Collective Motion, and Violent Flows crowd datasets. The proposed work is compared with various state-of-the-art methods and achieves remarkable accuracy while maintaining the lower computational complexity.

中文翻译：

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通过活动轮廓中的轨迹聚类来分割人群流

人群分析已成为视觉监控社区的一个重要研究课题。本文提出了一种基于活动轮廓的轨迹聚类方法用于人群流分割。为此，应用活动轮廓方法对前景人群区域进行分割，以优化进一步跟踪。从分割的前景区域，检测和跟踪时空兴趣点以提取人群轨迹。然后通过轨迹的形状、位置信息、流向和邻域密度对轨迹进行参数化。设计聚类算法来对这些轨迹进行聚类，并根据轨迹簇的空间重叠和位置和流向的差异，通过合并轨迹簇来分割进一步的流型。一旦流型被分割，估计每个段的轨迹密度以分析人群流量。在三个公开可用的 UCF Web、Collective Motion 和 Violent Flows 人群数据集上进行了实验。所提出的工作与各种最先进的方法进行了比较，并在保持较低的计算复杂度的同时实现了显着的准确性。