City Tracker: Multiple Object Tracking in Urban Mixed Traffic Scenes Multiple object tracking (MOT) in urban traffic is exciting since there is large variation of road user appearance. In order to overcome that variation while tracking the objects, a deep learning detectionbased tracking framework is implemented to employ classification information and associate different objects. In this paper, a method called City Tracker is proposed for MOT task in urban mixed traffic using urban traffic dataset. City Tracker contains two components which are YoloV3 detection and DeepSORT tracking. This method compares favorably to a current featurebased tracker for urban traffic scenes and achieves up to 0.8989 in MOTP and 0.4265 in MOTA.

UADETRAC: A new benchmark and protocol for multiobject detection and tracking Abstract   Effective multiobject 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 (UADETRAC) dataset for comprehensive performance evaluation of MOT systems especially on detectors. The UADETRAC benchmark dataset consists of 100 challenging videos captured from realworld traffic scenes (over 140,000 frames with rich annotations, including illumination, vehicle type, occlusion, truncation ratio, and vehicle bounding boxes) for multiobject detection and tracking. We evaluate complete MOT systems constructed from combinations of stateoftheart 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.