Simple Online and Realtime Tracking
This work provides a fast and efficient solution for realtime object tracking, though it is incremental as it uses familiar techniques like Kalman Filter and Hungarian algorithm.
The paper tackled the problem of efficient multiple object tracking for online and realtime applications by focusing on detection quality, which improved tracking by up to 18.9%, and achieved a tracker update rate of 260 Hz, over 20x faster than state-of-the-art trackers.
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.