Depth-directed hardware object detection

Object detection is a vital task in several emerging applications, requiring real-time detection frame-rate and low energy consumption for use in embedded and mobile devices. This paper proposes a hardware-based, depth-directed search method for reducing the search space involved in object detection, resulting in significant speed-ups and energy savings. The proposed architecture utilizes the disparity values computed from a stereoscopic camera setup, in an attempt to direct the detection classifier to regions that contain objects of interest. By eliminating large amounts of search data, the proposed system achieves both performance gains and reduced energy consumption. FPGA simulation results indicate performance speedups up to 4.7 times and high energy savings ranging from 41-48%, when compared to the traditional sliding window approach.