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 in-creased inference time and model complexity. The results highlight an explicit accuracy–efficiency trade-off.