Density-aware person detection and tracking in crowds

Mikel D. Rodriguez, I. Laptev, Josef Sivic, Jean-Yves Audibert
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引用次数: 355

Abstract

We address the problem of person detection and tracking in crowded video scenes. While the detection of individual objects has been improved significantly over the recent years, crowd scenes remain particularly challenging for the detection and tracking tasks due to heavy occlusions, high person densities and significant variation in people's appearance. To address these challenges, we propose to leverage information on the global structure of the scene and to resolve all detections jointly. In particular, we explore constraints imposed by the crowd density and formulate person detection as the optimization of a joint energy function combining crowd density estimation and the localization of individual people. We demonstrate how the optimization of such an energy function significantly improves person detection and tracking in crowds. We validate our approach on a challenging video dataset of crowded scenes.
人群中具有密度意识的人员检测和跟踪
我们解决了拥挤视频场景中的人员检测和跟踪问题。虽然近年来对单个物体的检测已经有了很大的提高,但由于严重的遮挡、高密度的人群以及人们外表的显著变化,人群场景的检测和跟踪任务仍然具有特别大的挑战性。为了应对这些挑战,我们建议利用关于场景全局结构的信息,并联合解决所有检测。特别是,我们探索了人群密度所施加的约束,并将人员检测定义为将人群密度估计与个体定位相结合的联合能量函数的优化。我们演示了这种能量函数的优化如何显着提高人群中的人员检测和跟踪。我们在一个具有挑战性的拥挤场景视频数据集上验证了我们的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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