Multi-human Tracking Across Multi-cameras

Human tracking has been a hot spot in the field of computer vision for a long time. It is to simulate the function of human vision using the computer and relative device.It is an extremely challenging research and comprehensive subject. The technolegy of human tracking is still in the course of research and exploration at present. There are many problems to be sloved in the aspect of theory and practical application. The reasons such as the motion complexity of human, alteration of illumination intensity and others influences the robustness of tracking human to a great extent.The dissertation mainly research on human tracking in a single camera and across multiple cameras. The human tracking across multiple cameras is on the basis of human tracking in a single camera. Only on the accurate premise of tracking human in a single camera, the tracking human across multiple cameras may be correct result. The dissertation mainly solves the tracking human under occlusion in a single camera. There are two cases of tracking human across multi-cameras.One is human tracking across multiple cameras with overlapping field of view.The other is human tracking across multiple cameras with non-overlapping field of view. The two cases will be talked about in the dissertation.1. The dissertation aims at solving the problems of tracking occluded human object. An algorithm is proposed based on particle filter of non-parameter mixture model which is described the multiple human objects in a single camera. The mixture model carries on two steps of mixture predict and mixture update by turn in order to accomplish recursive human tracking.During the course of mixture particle filter, the contribution of each particle to final object is weighed using weight of each particle.Each particle weight gets attained utilizing mutiple observation model of HSV histogram. Observation model gets acquired by kernel estimation of Bhattacharyya distance.The experiment shows that particle filter of mixture model can better track multiple human object under occlusion.2. The dissertatation makes full use of motion information,color information and space information of human object in a single camera.The dissertation effectively solves the problems of human tracking under occlusion each other using background modeling,blob modeling,color modeling,motion modeling and sapce information of human body. The dissertation firstly detect the kinetic persons using motion information to reconstruct background of video based on algorithm of mixture Gauss modeling. We will cluster to human body in storage based on Epanechnikov kernel density gradient estimation.Namely, we will obtain blob models,which put the color-similar pixels of human body together.We will establish the color density function based on non-parameter Gauss kernel density estimation and space information of human body, build motion density function based on motion information of human and obtain posterior probability utilizing color density function and motion density function. We apply maximum posterior probability to each pixel of detected human in current frame,obtain color image of maximum posterior probability and segment the color image to aim at tracking multiple human under occlusion in a single camera.3. The dissertation aims at researching on mis-match problem of tracking human object across cameras with overlapping field of view. A fusion algorithm is proposed based on space map transformation and color feature to accomplish match across cameras.The dissertation firstly ascertains the position of human across camera roughly using the common boundary lines of overlapping field view.Then the person across cameras gets matched by means of homography transformation matrix.However,in practical application,the videos across cameras is asynchronous and occluded persons, so there exist some mis-matches.The dissertation calibrates the mis-match by maximum prosterior probability of color model on the basis of homography matrix.4. The dissertation aims at solving mis-match problem caused by color differences across cameras with non-overlapping field of view. An algorithm is presented to calibrate the color pixels based on color transfer function and apply probability estimation to the space of color transfer function in order to advance match accuracy of tracking human across cameras.The algorithm firstly trains the given samples of human objects across cameras in a low dimension to acquire color transfer function.The algorithm is not dependent on internal parameter of cameras to acquire color transfer function.Then the probability estimation function is modeled utilizing principal compoment analysis to sub-space of color transfer function.Finally,match probability of tracking human across cameras using probability estimation function. The experiments in the case of outdoor-outdoor and indoor-outdoor shows the algorithm can better track human object across camera with non-overlapping field of view.