Detecting Major Segmentation Errors for a Tracked Person Using Colour Feature Analysis

Abstract

This paper presents a method to identify frames with significant segmentation errors in an individual's track by analysing the changes in appearance and size features along the frame sequence. The features used and compared include global colour histograms, local histograms and the bounding box' size. Experiments were carried out on 26 tracks from 4 different people across two cameras with differing illumination conditions. By fusing two local colour features with a global colour feature, probabilities of segmentation error detection as high as 83 percent of human expert-identified major segmentation errors are achieved with false alarm rates of only 3 percent. This indicates that the analysis of such features along a track can be useful in the automatic detection of significant segmentation errors. This can improve the final results of many applications that wish to use robust segmentation results from a tracked person.