Effective multi-object tracking (MOT) methods have been developed in recent years for a wide range of applications including visual surveillance and behavior understanding. Existing performance evaluations of MOT methods usually separate the tracking step from the detection step by using one single predefined setting of object detection for comparisons. In this work, we propose a new University at Albany DEtection and TRACking (UA-DETRAC) dataset for comprehensive performance evaluation of MOT systems especially on detectors. The UA-DETRAC benchmark dataset consists of 100 challenging videos captured from real-world traffic scenes (over 140,000 frames with rich annotations, including illumination, vehicle type, occlusion, truncation ratio, and vehicle bounding boxes) for multi-object detection and tracking. We evaluate complete MOT systems constructed from combinations of state-of-the-art object detection and tracking methods. Our analysis shows the complex effects of detection accuracy on MOT system performance. Based on these observations, we propose effective and informative evaluation metrics for MOT systems that consider the effect of object detection for comprehensive performance analysis.

近年来，已经开发出了有效的多目标跟踪（MOT）方法，其广泛应用包括视觉监视和行为理解。现有的MOT方法性能评估通常通过使用对象检测的一个预定义设置进行比较来将跟踪步骤与检测步骤分开。在这项工作中，我们提出了一个新的奥尔巴尼大学检测与跟踪（UA-DETRAC）数据集，用于对MOT系统（尤其是在探测器上）进行全面性能评估。UA-DETRAC基准数据集包含从现实交通场景中捕获的100个具有挑战性的视频（超过140,000帧，带有丰富的注释，包括照明，车辆类型，遮挡，截断率和车辆边界框），用于多对象检测和跟踪。我们评估由最先进的物体检测和跟踪方法组合而成的完整MOT系统。我们的分析表明检测精度对MOT系统性能的复杂影响。基于这些观察结果，我们为MOT系统提出了有效且信息丰富的评估指标，该指标考虑了对象检测对综合性能分析的影响。