Multi Cue Performance Evaluation Metrics for Tracking in Video Sequences

Abstract

The key issue addressed by this paper is the necessity to devise performance evaluation measures for systems that integrate multiple cues for tracking in video sequences. We propose a generic evaluation approach that can be implemented in systems that perform higher-level people tracking by integrating multiple low-level features extracted from the video data. Two new measures: video sequence accuracy (VSA) and voting average measure (VAM), are introduced and explained by using the two fundamental image processing techniques of edge and optical flow detection. The effectiveness of the approach is demonstrated using a set of real video sequences with ground truth.