Multiple Object Tracking Performance Metrics and Evaluation in a Smart Room Environment

Simultaneous tracking of multiple persons in real world environments is an active research field and several approaches have been proposed, based on a variety of features and algorithms. Recently, there has been a growing interest in organizing systematic evaluations to compare the various techniques. Unfortunately, the lack of common metrics for measuring the performance of multiple object trackers still makes it hard to compare their results. In this work, we introduce two intuitive and general metrics to allow for objective comparison of tracker characteristics, focusing on their precision in estimating object locations, their accuracy in recognizing object configurations and their ability to consistently label objects over time. We also present a novel system for tracking multiple users in a smart room environment using several cameras, based on color histogram tracking of person regions and automatic initialization using special object detectors. This system is used to demonstrate the expressiveness of the proposed metrics through a sample performance evaluation using real test video sequences of people interacting in the