UA-DETRAC: A New Benchmark and Protocol for Multi-Object Detection and Tracking

TL;DR

This paper introduces UA-DETRAC, a comprehensive benchmark dataset for multi-object detection and tracking in traffic scenes, and analyzes how detection accuracy impacts overall tracking performance.

Contribution

It provides a large-scale, real-world dataset and evaluates the interplay between detection and tracking, proposing new metrics for comprehensive MOT system assessment.

Findings

Detection accuracy significantly affects MOT performance.

New evaluation tools better capture detection-tracking interactions.

Benchmark enables systematic comparison of MOT methods.

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 DETection 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.