An Adaboost-based Two-level Moving Object Detection Architecture with Dynamic ROI Allocation

Abstract

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 14x28 to 40x80, the proposed design supports detection at a distance as far as 40 meters.