To detect multiple objects of interest, the methods based on Hough transform use non-maxima supression or mode seeking in order to locate and to distinguish peaks in Hough images. Such postprocessing requires tuning of extra parameters and is often fragile, especially when objects of interest tend to be closely located. In the paper, we develop a new probabilistic framework that is in many ways related to Hough transform, sharing its simplicity and wide applicability. At the same time, the framework bypasses the problem of multiple peaks identification in Hough images, and permits detection of multiple objects without invoking nonmaximum suppression heuristics. As a result, the experiments demonstrate a significant improvement in detection accuracy both for the classical task of straight line detection and for a more modern category-level (pedestrian) detection problem.
Iterations of the Hough-like algorithm for multiple object instances detection:
We used image datasets TUD-crossing and TUD-campus containing mostly profile views of pedestrians in relatively crowded locations. The original annotations provided include only the pedestrians occluded by no more than 50%. As we were interested in the performance of the method under significant overlap, we reannotated the data by marking all pedestrians whose head and at least one leg were clearly visible.
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Download c++ code for pedestrians detection
Download c++ code for lines detection
Slides from CVPR 2010 [zip]
Talk at CVPR 2010 [link]
This project is supported by Microsoft Research programs in Russia. V. Lempitsky is also supported by EU under ERC grant VisRec no. 228180.