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

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.