SORT-LFR: Revisiting SORT for Multi-Object Tracking in Low-Frame-Rate Videos

For certain applications like highway surveillance systems, only low-frame-rate videos are recorded, which presents a huge challenge to existing trackers, as objects tend to undergo far more abrupt changes in location, motion, and appearance between successive frames compared to normal frame rates. To handle the above challenges, we propose a novel approach, namely$\mathbb {SORT}$-$\mathbb {LFR}$, for$\mathbb {S}$imple$\mathbb {O}$nline and$\mathbb {R}$ealtime$\mathbb {T}$racking in$\mathbb {L}$ow-$\mathbb {F}$rame-$\mathbb {R}$ate videos, which consists of following techniques: 1) A feature-prior association strategy to improve the capability to track new objects with significant displacements; 2) A Kalman filter using acceleration in state space (accel-fused Kalman filter) to improve the motion estimation capability for non-constant velocity moving objects; 3) A detection-guided adaptive exponential moving average (DG-AEMA) feature update mechanism to enhance feature temporal modeling capability for tracked objects; 4) A trajectory-covariance threshold tuning (TCTT) method to filter out incorrect association results. Through these techniques, the proposed SORT achieves 91.8 HOTA, 92.6 MOTA and 93.9 IDF1, which surpass all state-of-the-art trackers on the public CityFlow and our private HighwayTrack datasets under the low-frame-rate setting.