Multiple vehicle segmentation and tracking in complex environments

UnrestrictedOur goal is to detect and to track multiple moving vehicles observed from static surveillance cameras, which are usually placed on poles or buildings. Methods of background subtraction are widely used in these kinds of conditions. But to extract vehicle information from motion foreground, common difficulties, such as noise foreground, shadow, scene occlusion, blob merge and blob split, have to be solved. By using vehicle shape models, in addition to camera calibration and ground plane knowledge, the proposed methods can detect, track and classify moving vehicles in the presence of all these difficulties.; Two methods are proposed in this thesis to deal with related problems. The first method uses dynamic background model to extract the motion foreground. The models of camera and vehicle are used to reduce the foreground noise. Spatial and temporal constraints are applied to handle blob split, and object color appearance is used to track each vehicle when multiple vehicles are merged together. Evaluation on a large dataset by a third party shows that this method works robustly under many conditions.; The second method focuses on challenging tracking situations where vehicle inter-occlusion is prevalent and persistent. In this case, each foreground blob can contain multiple vehicles. Simple one-to-one correspondence between the foreground blobs and vehicles does not apply any more. Segmentation of the merged vehicles is a difficult problem. This proposed method works in the framework of Markov chain Monte Carlo (MCMC) approach. By sampling in the multi-vehicle configuration space, the method searches for the set of vehicle parameters, that best explains the foreground. Several bottom-up detections are utilized with top-down analysis to guide the sampling in an effective way.; The goal of this work is to infer the trajectory of each individual vehicle. Because of the approximation of vehicle models and the limitation of the likelihood function, the multi-vehicle configuration with the highest probability may not always be the correct segmentation. By exploring the spatial and temporal constraints across the image sequences, a tracking method is proposed to reduce the errors on single frame vehicle detection