Algorithm Research On Multiple Visual Target Tracking

Multi-target tracking (MTT) is one of the most active research subjects in the area of computer vision research. It syncretizes advanced technologies in many fields such as image processing, pattern recognition, artificial intelligence, automatic control and computer science. With the rapid development of modern computer science and information technology, and with the marvelous renovation of image identification, MTT comes out into the open and is of great practical value in the field of military defence, medical research, traffic monitoring, astronomical prediction, intelligent supervision etc.The paper firstly introduces several common algorithms in visual object detection--the frame difference, background subtraction and so on, and then investigates the Gaussian background Mixture Model (GMM) and the updating algorithm, make a model for each pixel to calculate the probability of background pixel in the current image so as to abstract foreground moving objects. We create a color model of brightness distortion and chrominance distortion about the image to distinguish shaded background. The experiments indicate that this algorithm can distinguish foreground objects and its shadow and increase the efficiency of tracking algorithm. Based on the background modeling, this paper detailed investigates the particle filtering used in the object tracking, analyzes the research situation of the particle filter, put forward a Genetic Resampling Particle Filter Algorithm which takes the genetic algorithm into the particle mechanism, and devise the applicability function using the special distance between the target and the estimate to track the multiple objects, The experiments prove that this algorithm can realize the effective tracking of multiple target.