Interactive Media Systems, TU Vienna

Robust detection and tracking of objects

Thesis by Florian Seitner

Supervision by Allan Hanbury


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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