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 × 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 pre-processing method called Hierarchical HOG Matrices (HHM) in place of integral histogram. Our speed-up scheme can process 320 × 240 sized images by dense scan (≈ 12000 windows per image) at the speed of about 30fps, while maintaining accuracy comparable to the original HOG + cascade method.