Multi-target tracking based on cluster min-distance data association

A multi-target tracking algorithm based on cluster min-distance data association was proposed. Three matching distances about target position, target size and target gray value were established, weighted and integrated into final target matching distance function. The algorithm searched for the target list's follow-up data which had min-matching distance relationship with observed target and the observed target's precursor data which had min-matching distance relationship with target list. Only when the follow-up relationship and the precursor relationship were both true, the matching relationship was true between them. All the target lists were divided into four clusters. All the observed targets were divided into two clusters. The probable relationships between all clusters were analyzed, with carrying out the above computation. If the target was blocked, a target forecasting algorithm was executed. Based on the theory of instantaneous linear and mean filtering, the position data of target list were input to gian a regression line for forecasting the target's position of next frame. The target's size and gray value data were gained by mean filtering for the moment. A good result was presented on multi-human tracking.