ABSTRACT
Object tracking is the main task in the field of computer vision. This chapter proposes and compares a new method for recognition and tracking by using CamShift and color detection. All the presented algorithms were programmed with Python programming language supported by OpenCV libraries, and executed with a credit card–size computer board called Raspberry Pi with attached external camera. A camera mimics and uses real-time digital videos for object recognition and tracking. The chapter also proposes a color detection algorithm in very low light conditions is proposed for the enhancement of target tracking. Mean shift is a nonparametric feature-space analysis technique for locating the maxima of a density function. The CamShift algorithm is dependent on an adaptation of mean shift algorithm. Histogram equalization is a nonlinear technique for adjusting the contrast of an image using its histogram.
