Performance enhancement of multi-human tracking based on K-Shortest Paths by data reduction

Abstract

Object tracking is a challenging problem and it has been improving dramatically in recent years. In this paper, we perform parallelized multi-object tracking system. Object tracking problem has 2 difficulties; one is to detect objects collect, and the other is to track collect using the collect object detection. Jerome et al. performed a multi-object tracking system using K-Shortest Paths to avoid these problems efficiently. However, it is difficult to calculate in parallel because of the iterations calculation of shortest paths on the graph while changing the weight of graph. In our method, we divided time intervals to apply KSP method from Probability Occupancy Map(POM), which is also obtained via using KSP method. Performance evaluation shows our algorithm is 5.4 times faster than the original KSP with 87% accuracy.