Enhancing QDTrack with Self-Attention in Autonomous Driving Environments

Reliable and safe navigation of self-driving cars requires multi-object tracking algorithms to estimate the trajectories of moving objects on the road. The performance of tracking algorithms can be improved by optimizing each component of the detector-tracker pipeline. A valuable method to improve detectors is exploiting attention mechanisms, which imitate how humans find salient regions in a scene. In this paper, we have integrated self-attention mechanisms into Faster R-CNN, the detector included in QDTrack, a state-of-the-art tracker that follows the tracking-by-detection paradigm. We have evaluated the performance of the enhanced multi-object tracking system on the BDD100K dataset. Results show that integrating attention mechanisms into the detector improves QDTrack tracking performance, particularly in terms of mMOTA, at the cost of increased inference time and model complexity. The results highlight an explicit accuracy–efficiency trade-off.