Abnormal work cycle detection based on dissimilarity measurement of trajectories

Abstract

This paper proposes a method for detecting the abnormalities of the executed work cycles for the factory workers using their tracks obtained in a multi-camera network. The method allows analyzing both spatial and temporal dissimilarity between the pairwise tracks. The main novelty of the methods is calculating spatial dissimilarity between pair-wise tracks by aligning them using Dynamic Time Warping (DTW) based on coordinate distance, and specially the velocity and dwell time dissimilarity using a different track alignment based on velocity difference. These dissimilarity measurements are used to cluster the executed work cycles and detect abnormalities. The experimental results show that our algorithm outperforms other methods on clustering the tracks because of the use of temporal dissimilarity.