An Adaboost-based two-level moving object detection architecture with dynamic ROI allocation
Jui-Sheng Lee, Hsiu-Cheng Chang, Jiun-In Guo · 2014
This paper presents a hardware architecture to detect moving objects based on Adaboost algorithm [1] with Haar-like feature for driving safety. According to the complex appearances of pedestrians and motorcyclists at closer distance, the proposed design supports 12-level scaling for detection window size with dynamic ROI allocation. The stride mode results in highly complex 12-level window size scaling. So a two-level fast search method for decreasing hardware cost while preserving 93.6% detection rate is proposed. The proposed design comprises of 173K gates and 35.7Kbytes SRAM. The maximum working frequency is 200MHz that is able to process VGA@31fps and QVGA@107fps input video. With the detection window sizes scaled from 14×28 to 40×80, the proposed design supports detection at a distance as far as 40 meters.