Tracking Multiple Pedestrians From Monocular Videos In An Interacting Multiple Model Framework
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Tracking Multiple Pedestrians from Monocular Videos in an Interacting Multiple Model Framework
[Truncated abstract] Detecting and tracking pedestrians in videos have many important computer vision applications including visual surveillance, people recognition, smart environments and human-machine interaction. An automatic pedestrian tracking system comprises 2 main stages: (1) detecting pedestrians in the initial frame or every frame; (2) maintaining the identities of pedestrians in video sequences. Tracking pedestrians in videos is a challenging problem because of background clutters, lighting variation, and occlusion. A reliable pedestrian tracking system should be capable of maintaining the identities of pedestrians in the presence of partial or even, occasionally, full occlusion in video sequences. The appearance model of a pedestrian is widely used in pedestrian tracking. For the appearance model, I compute the colour histograms for the upper and lower bodies of each pedestrian detected by the Histogram of Oriented Gradient (HOG) human detector. Each of these colour histograms is a 3-dimensional entity in the L*a*b colour space and the concatenation of them yields a 4-dimensional tensor. This allows spatial information to be incorporated into the appearance model while the computation cost is kept to its minimum. To get a better estimate of the colour histogram, the kernel density estimation is used to smooth the histogram of the appearance of each pedestrian. The Hellinger distance is used for histogram matching. In this thesis, a multiple pedestrian tracking method for monocular videos captured by a fixed camera in an interacting Multiple Model (IMM) framework is presented. This tracking method involves multiple IMM trackers running in parallel which are tied together by a robust data association component. This method formulates data association as a bipartite graph problem and employs the Munkres' algorithm to associate the new observations with the existing tracks. The data association algorithm incorporates the information from the appearance and the motion models to get the edge weights of the graph. Two strategies, thresolding on the total error (TTE) and validation gate and appearance error (VAE), for data association are designed and compared. The first strategy is to impose a threshold value on the total error from the appearance and motion models as the edge weights of the bipartite graph. The second strategy is to use the combined motion model as a validation gate and the error from the appearance model only as the edge weights. The experimental results show that the second strategy gives better tracking results. Short-term occlusion problems and false negative errors from the detector are dealt with using a sliding window of video frames where tracking persists in the absence of observations. For the interacting multiple model tracking framework, the method incorporates three motion models: a stationary model, a constant velocity model and a constant acceleration model. This method has been evaluated and compared both qualitatively and quantitatively with three common tracking methods using various standard video databases...
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