Hough transform based object detectors divide an object into a number of patches and combine them using a shape model. For efficient combination of patches into the shape model, the individual patches are assumed to be independent of one another. Although this independence assumption is key for fast inference, it requires the individual patches to have a high discriminative power in predicting the class and location of objects. In this paper, we argue that the sparsity of the appearance of a patch in its neighborhood can be a very powerful measure to increase the discriminative power of a local patch and incorporate it as a sparsity potential for object detection. Further, we show that this potential shall depend on the appearance of the patch to adapt to the statistics of the neighborhood specific to the type of appearance (e.g. texture or structure) it represents. We have evaluated our method on challenging datasets including the PASCAL VOC 2007 dataset and show that using the proposed sparsity potential result in a substantial improvement in the detection accuracy.
The similarity of a voting patch with the patches in its neighborhood can be used to improve detection performance.
Razavi N., Alvar N., Gall J., and van Gool L., Sparsity Potentials for Detecting Objects with the Hough Transform (PDF), British Machine Vision Conference (BMVC'12), 2012.
Gall J., Yao A., Razavi N., van Gool L., and Lempitsky V., Hough Forests for Object Detection, Tracking, and Action Recognition (PDF), IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 33, No. 11, 2188-2202, 2011. ©IEEE