#195: Robust multi-object tracking with semantic color correlation


N. M. Al-Shakarji, F. Bunyak, G. Seetharaman, and K. Palaniappan

IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 2017

image color analysis, tracking, color, correlation, lighting, histograms.

PlainText, Bibtex, URL, DOI, Google Scholar

Abstract

Multi-object tracking is an important computer vision task with wide variety of real-life applications from surveillance and monitoring to biomedical video analysis. Multi-object tracking is a challenging problem due to complications such as partial or full occlusions, factors affecting object appearance, object interaction dynamics, etc. and computational cost. In this paper, we propose a detection-based multi-object tracking system that uses a two-step data association scheme to ensure time efficiency while preserving tracking accuracy; a robust but discriminative object appearance model that compares object color attributes using a novel color correlation cost matrix; and a framework that handles occlusions through prediction. Our experiments on UA-DETRAC multi-object tracking benchmark dataset consisting of challenging real-world traffic videos show promising results against state-of-the-art trackers.