A hardware-oriented histogram of oriented gradients algorithm and its VLSI implementation

A challenging and important issue for object recognition is feature extraction on embedded systems. We report a hardware implementation of the histogram of oriented gradients (HOG) algorithm for real-time object recognition, which is known to provide high efficiency and accuracy. The developed hardware-oriented algorithm exploits the cell-based scan strategy which enables image-sensor synchronization and extraction-speed acceleration. Furthermore, buffers for image frames or integral images are avoided. An image-size scalable hardware architecture with an effective bin-decoder and a parallelized voting element (PVE) is developed and used to verify the hardware-oriented HOG implementation with the application of human detection. The fabricated test chip in 180 nm CMOS technology achieves fast processing speed and large flexibility for different image resolutions with substantially reduced hardware cost and energy consumption.