Real-Time Human Detection Using Hierarchical HOG Matrices
Human detection has witnessed significant development in recent years. The introduction of cascade structure and integral histogram has greatly improved detection speed. But real-time detection is still only possible for sparse scan of 320 x 240 sized images. In this work, we propose a matrix-based structure to reorganize the computation structure of window-scanning detection algorithms, as well as a new preprocessing method called Hierarchical HOG Matrices (HHM) in place of integral histogram. Our speed-up scheme can process 320 x 240 sized images by dense scan (approximate to 12000 windows per image) at the speed of about 30 fps, while maintaining accuracy comparable to the original HOG + cascade method.
human detection hog real-time detection window-scanning multi-detector