Detection and tracking using multi-color target models

Abstract

This paper presents object detection and tracking algorithm which can adapt to object color shift. In this algorithm, we train and build multi target models using color under different illumination conditions. Each model called as Color Distinctiveness look up Tables or CDT. The color distinctiveness is the value integrating 1) similarity with target colors and 2) dissimilarity with non-target colors, which represents how distinctively the color can be classified into target pixel. Color distinctiveness can be used for pixel-wise target detection, because it takes 0.5 for colors on decision boundary of nearest neighbor classifier in color space. Also, it can be used for target tracking by continuously finding the most distinctive region. By selecting the most suitable CDT for camera direction, lighting condition, and camera parameters, the system can adapt target and background color change. We implemented this algorithm for a Pan-tilt stereo camera system. Through experiments using this system, we confirmed that this algorithm is robust against color shift caused by illumination change and it can measure the target 3D position at video rate.