2D-object tracking based on projection-histograms
Image-sequence analysis for real-time applications requires high quality and highly efficient algorithms for tracking as there is no time to do the costly object recognition each time a new image is captured. Tracking with projection histograms revealed amazing results compared with standard correlation methods. Trackers based on projection histograms performed 31% up to 211% better than the reference methods on a common test set. The new template-based method relying on projection histograms (RPH) is described and compared with two commonly known template based methods namely the normalized cross-correlation (NCC) and displaced-frame-distance (DFD) methods. The input to the system consists of live or recorded video data where filterbased preprocessing can be applied before tracking in order to enhance features such as edges, textures etc. A region of interest (ROI) is taken as a template for tracking. In subsequent images tracking exploits a Kalman-filtered local search in order to renew correspondence between the object template and the new object location. Comparative tests were performed with real-live image-sequences taken in underground stations. Tracking with projection histograms outperformed tracking by NCC and DFD on grey-level image-sequences as well as on edge-enhanced image-sequences. Even the worst chosen parameter set for tracking by the new RPH method resulted in better tracking as with the best ones for both NCC and DFD.
Keywordstemplate matching 2D-tracking real-time tracking
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