FRoG-MOT: Fast and Robust Generic Multiple-Object Tracking by IoU and Motion-State Associations

This paper proposes a generic multi-object tracking (MOT) algorithm that is robust to unexpected motion changes for generic objects. Deep learning has dramatically been improving MOT performances. Nevertheless, state-of-the-art tracking algorithms are still sensitive to unexpected motion changes and the generic object target beyond person tracking. This is because standard MOT benchmark datasets such as MOT17 mainly consist of persons in a crowd, often lacking unexpected shape and motion changes; thus, these issues have yet to be focused on. We propose a simple-yet-effective MOT framework that can dynamically improve tracking continuity by associating each target based on adaptively modified motion states. The keys are 1) to represent the target motions using multiple motion states that have weak correlations with each other and 2) to modify those states that have the lowest similarity to past states as outliers. Our approach can improve trajectory continuity and robustness to unexpected motion changes for generic objects. Comprehensive experiments have confirmed that our framework is comparable to existing state-of-the-art methods on a standard dataset and outperforms those algorithms on the GMOT dataset with an overall 2% improvement in IDF1, a measure of tracking continuity.