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Hough Forests have demonstrated effective performance in object detection tasks, which has potential to translate to exciting opportunities in pattern search. However, current systems are incompatible with the scalability and performance requirements of an interactive visual search. In this paper, we pursue this potential by rethinking the method of Hough Forests training to devise a system that is synonymous with a database search index that can yield pattern search results in near real time. The system performs well on simple pattern detection, demonstrating the concept is sound. However, detection of patterns in complex and crowded street-scenes is more challenging. Some success is demonstrated in such videos, and we describe future work that will address some of the key questions arising from our work to date.

Rethinking random Hough Forests for video database indexing and pattern search