IEEE Trans Pattern Anal Mach Intell. 2011 Sep;33(9):1820-33. doi: 10.1109/TPAMI.2010.232. Epub 2010 Dec 23.
In this paper, we address the problem of automatically detecting and tracking a variable number of persons in complex scenes using a monocular, potentially moving, uncalibrated camera. We propose a novel approach for multiperson tracking-by-detection in a particle filtering framework. In addition to final high-confidence detections, our algorithm uses the continuous confidence of pedestrian detectors and online-trained, instance-specific classifiers as a graded observation model. Thus, generic object category knowledge is complemented by instance-specific information. The main contribution of this paper is to explore how these unreliable information sources can be used for robust multiperson tracking. The algorithm detects and tracks a large number of dynamically moving people in complex scenes with occlusions, does not rely on background modeling, requires no camera or ground plane calibration, and only makes use of information from the past. Hence, it imposes very few restrictions and is suitable for online applications. Our experiments show that the method yields good tracking performance in a large variety of highly dynamic scenarios, such as typical surveillance videos, webcam footage, or sports sequences. We demonstrate that our algorithm outperforms other methods that rely on additional information. Furthermore, we analyze the influence of different algorithm components on the robustness.
在本文中，我们解决了使用单目、潜在移动、未校准的摄像机在复杂场景中自动检测和跟踪可变数量的人的问题。我们提出了一种新的基于粒子滤波框架的多人跟踪检测方法。除了最终的高置信度检测结果外，我们的算法还使用行人检测器的连续置信度和在线训练的、特定于实例的分类器作为分级观测模型。因此，通用对象类别知识被实例特定信息补充。本文的主要贡献在于探索如何利用这些不可靠的信息源进行稳健的多人跟踪。该算法可以在具有遮挡的复杂场景中检测和跟踪大量动态移动的人，不依赖于背景建模，不需要摄像机或地面平面校准，并且只利用过去的信息。因此，它施加的限制很少，适用于在线应用。我们的实验表明，该方法在各种高度动态的场景中都能取得良好的跟踪效果，例如典型的监控视频、网络摄像头视频或运动序列。我们证明了我们的算法优于其他依赖于附加信息的方法。此外，我们还分析了不同算法组件对鲁棒性的影响。
