Due to the increasing availability of fast and cheap hardware in the past few years, today a wide range of complex visual tracking tasks is possible. E cient mathematical methods can provide a high robustness which also makes visual tracking interesting for many industrial purposes. However, the high demands on quality and speed still provide a major challenge for each tracking application. In this thesis a tracking system is introduced, which tries to address both demands appropriately by using currently available algorithms to quickly track pedestrians in video streams. By combining these well-proved algorithms, a good solution regarding computational complexity, accuracy and stability is obtained. To achieve this task, a fast object detector similar to the approach of Viola et al. [VJS03] is used as one component in this tracking system. This detector uses Haar-like features which are very fast to compute and makes a quick pedestrian detection in a frame possible. Next to the detection system, an adaptive background model sub-divides each frame into foreground and background regions. As a compromise between complexity and robustness a single-mode parametric background model based on normal distributions and wrapped normal distributions is used. Both background model and detector are combined to provide the tracking system with locations of pedestrian-like regions and to sub-divide the body into three parts: head, upper body and lower body. After this segmentation into ner tracking units a set of colour and spatial features for further tracking is extracted from each part individually. Individual and spatially separated body parts also provide the possibility to use colour histograms in a spatial sense. Moreover, an appearance model provides accurate solutions and approximations when occlusions or missing detections occur.
F. Seitner: "Robust detection and tracking of objects"; Supervisor: A. Hanbury; Institut für rechnergestützte Automation, 2006.
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