Multi-appearance Segmentation and Extended 0-1 Program for Dense Small Object Tracking
This addresses tracking challenges for dense small objects in complex backgrounds, which is an incremental improvement in computer vision.
The paper tackles fast multi-object tracking for dense small objects in cluster backgrounds by developing a multi-appearance segmentation method for detection and a new one-to-many constraint to handle track exclusions during occlusions. Experimental results show the tracker achieves speed and performance advantages.
Aiming to address the fast multi-object tracking for dense small object in the cluster background, we review track orientated multi-hypothesis tracking(TOMHT) with consideration of batch optimization. Employing autocorrelation based motion score test and staged hypotheses merging approach, we build our homologous hypothesis generation and management method. A new one-to-many constraint is proposed and applied to tackle the track exclusions during complex occlusions. Besides, to achieve better results, we develop a multi-appearance segmentation for detection, which exploits tree-like topological information and realizes one threshold for one object. Experimental results verify the strength of our methods, indicating speed and performance advantages of our tracker.