IEEE Trans Pattern Anal Mach Intell. 2018 Mar;40(3):568-581. doi: 10.1109/TPAMI.2017.2687462. Epub 2017 Mar 24.
Multi-object tracking has been studied for decades. However, when it comes to tracking pedestrians in extremely crowded scenes, we are limited to only few works. This is an important problem which gives rise to several challenges. Pre-trained object detectors fail to localize targets in crowded sequences. This consequently limits the use of data-association based multi-target tracking methods which rely on the outcome of an object detector. Additionally, the small apparent target size makes it challenging to extract features to discriminate targets from their surroundings. Finally, the large number of targets greatly increases computational complexity which in turn makes it hard to extend existing multi-target tracking approaches to high-density crowd scenarios. In this paper, we propose a tracker that addresses the aforementioned problems and is capable of tracking hundreds of people efficiently. We formulate online crowd tracking as Binary Quadratic Programing. Our formulation employs target's individual information in the form of appearance and motion as well as contextual cues in the form of neighborhood motion, spatial proximity and grouping, and solves detection and data association simultaneously. In order to solve the proposed quadratic optimization efficiently, where state-of art commercial quadratic programing solvers fail to find the solution in a reasonable amount of time, we propose to use the most recent version of the Modified Frank Wolfe algorithm, which takes advantage of SWAP-steps to speed up the optimization. We show that the proposed formulation can track hundreds of targets efficiently and improves state-of-art results by significant margins on eleven challenging high density crowd sequences.
多目标跟踪已经研究了几十年。然而，当涉及到在极其拥挤的场景中跟踪行人时，我们只能依靠少数几个工作。这是一个重要的问题，它带来了几个挑战。预先训练的目标检测器无法在拥挤的序列中定位目标。这就限制了基于数据关联的多目标跟踪方法的使用，这些方法依赖于目标检测器的结果。此外，目标的实际尺寸较小，难以提取特征来区分目标与其周围环境。最后，大量的目标大大增加了计算复杂性，从而使得难以将现有的多目标跟踪方法扩展到高密度人群场景。在本文中，我们提出了一种跟踪器，该跟踪器能够有效地跟踪数百人。我们将在线人群跟踪表述为二进制二次规划问题。我们的公式采用了目标的个体信息，包括外观和运动，以及上下文线索，如邻域运动、空间接近度和分组，并同时解决检测和数据关联问题。为了有效地解决所提出的二次优化问题，其中最先进的商业二次规划求解器无法在合理的时间内找到解决方案，我们提出使用最新版本的修正 Frank Wolfe 算法，该算法利用 SWAP 步骤来加速优化。我们表明，所提出的公式可以有效地跟踪数百个目标，并在十一个具有挑战性的高密度人群序列上显著提高了最先进的结果。
