UA-DETRAC: A new benchmark and protocol for multi-object detection and tracking

Abstract

In recent years, numerous effective multi-object tracking (MOT) methods are developed because of the wide range of applications. Existing performance evaluations of MOT methods usually separate the object tracking step from the object detection step by using the same fixed object detection results for comparisons. In this work, we perform a comprehensive quantitative study on the effects of object detection accuracy to the overall MOT performance, using the new large-scale University at Albany DE-Tection and tRACking (UA-DETRAC) benchmark dataset. The UA-DETRAC benchmark dataset consists of 100 challenging video sequences captured from real-world traffic scenes (over 140, 000 frames with rich annotations, including occlusion, weather, vehicle category, truncation, and vehicle bounding boxes) for object detection, object tracking and MOT system. We evaluate complete MOT systems constructed from combinations of state-of-the-art object detection and object tracking methods. Our analysis shows the complex effects of object detection accuracy on MOT system performance. Based on these observations, we propose new evaluation tools and metrics for MOT systems that consider both object detection and object tracking for comprehensive analysis.

FAMNet: Joint Learning of Feature, Affinity and Multi-Dimensional Assignment for Online Multiple Object Tracking

“…UA-DETRAC. UA-DETRAC dataset [47] is another multiple vehicle tracking dataset with 60 sequences for training and 40 sequences for testing. All sequences are recorded with static camera at a lift-up position near different drive ways in various of weather conditions.…”

“…We conduct experiments on four popular MOT datasets: MOT2015 [28] and MOT2017 [30] for pedestrian tracking, KITTI-Car [17] and UA-DETRAC [47] for vehicle tracking. All datasets are provided with referred detections from real detectors.…”

Deep learning in video multi-object tracking: A survey

“…Such score was finally used by the Hungarian algorithm to perform association. The method reached top performance on the UA-DETRAC dataset [32], but the performance on MOT16 was not very good when compared with other algorithms using private detections.…”

“…Besides the previously described datasets, there is a number of older, and now less frequently used, ones. Among those we can find the UA-DETRAC tracking benchmark 9 [32], that focuses on vehicles tracked from traffic cameras, and the TUD 10 [33] and PETS2009 11 [34] datasets, that both focus on pedestrians. Many of their videos are now part of the MOTChallenge datasets.…”

Trained Model Fusion for Object Detection Using Gating Network

“…Dataset: We use a part of the UA-DETRAC [30] dataset that contains 100 video sequences captured from real-world traffic scenes at different locations. In this work, we select 4 location videos as target domains (T1, T2, T3, and T4) for inference ( Fig.…”