Simple Online and Realtime Tracking

TL;DR

This paper presents a simple, fast online tracking method that effectively associates objects in real-time, achieving high accuracy and speed by leveraging basic techniques and emphasizing detection quality.

Contribution

It introduces a straightforward online tracking approach that combines familiar methods, emphasizing detection quality, and achieves competitive accuracy with significantly higher processing speed.

Findings

Achieves accuracy comparable to state-of-the-art online trackers.

Runs at 260 Hz, over 20 times faster than existing methods.

Improves tracking performance by up to 18.9% through better detection quality.

Abstract

This paper explores a pragmatic approach to multiple object tracking where the main focus is to associate objects efficiently for online and realtime applications. To this end, detection quality is identified as a key factor influencing tracking performance, where changing the detector can improve tracking by up to 18.9%. Despite only using a rudimentary combination of familiar techniques such as the Kalman Filter and Hungarian algorithm for the tracking components, this approach achieves an accuracy comparable to state-of-the-art online trackers. Furthermore, due to the simplicity of our tracking method, the tracker updates at a rate of 260 Hz which is over 20x faster than other state-of-the-art trackers.