FocoTrack: Multi Object Tracking by Focusing on Overlap at Low Frame Rate

Multi-object tracking (MOT) presents a crucial challenge in robotics. Due to limited resources embedded in robots, one time step per processing time for algorithms can be considerably large. This scenario necessitates the operation of MOT at a low frame rate. However, algorithms within the MOT research field have been constructed around datasets functioning at 10–30 frames per second (fps) which can be difficult to operate in the limited resources. In response to it, we introduce a new algorithm, called FocoTrack, which maintains tracking ability in four situations, one of which is when objects are overlapped by each other. Our algorithm exhibits remarkable performance without using any deep appearance descriptor, surpassing existing MOT methods which even use the deep appearance descriptor on a 2.5 fps dataset. We also demonstrate strong results with our algorithm on DanceTrack dataset at 20 fps and provide comprehensive insights through detailed analysis of our tracking model.