Research And Implementation Of Multi-Target Tracking Technique Based On Particle Filtering And Background Modeling

Multi-target tracking (MTT) is one of the most active research subjects in the area of computer vision research. 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.Particle Filter, as a nonlinear filtering based on Bayesian estimate, has an advantage in the field of nonlinear moving target tracking. However, the framework of particle filter does not cover the mechanism of data association. When the target number changes or the targets shelter from each other during the process of multi-target tracking, target tracking will fail. Furthermore, the interference among some targets will influence the accuracy of tracking.Based on implementing target tracking by means of particle filtering, a technique framework of tracking target by integrating particle filtering and background modeling is presented. The multi-target tracking is classified into 5 modules as background modeling, multi-target tracking, initializing, re-initializing and particle filtering. The research results are as follows:The author models each pixel of the image with Gaussian Mixture Model (GMM), to calculate the probability of background pixel in the current image so as to abstract foreground moving objects. When processing the shadow of foreground moving objects, in order to overcome the influence of illumination change of part or integrity in the image, the color model of brightness distortion and chrominance distortion is established to distinguish shaded background, highlight background, original background and foreground objects, and then select the threshold automatically to classify the pixel points in the image. 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 article detailed investigates the algorithm flow and technique framework of generating the particle set of each object and particle filtering. Three particle resembling algorithm (select with replacement, linear time resembling, select with weight function) are realized. In the process of evaluating particle weight, in order to distinguish the different color features of the objects, the original algorithm (evaluating through Bhattacharyya distance) was improved. Only the color distribution of the foreground pixel in particle area after the background modeling is counted, therefore the accuracy and efficiency of target tracking are increased. By means of the results of background modeling, the connection between time and space of the moving targets, and belief theory, the problem of new target appearing and old targets disappearing can be solved. The experiments prove that this algorithm can realize the effective tracking of multiple moving persons.