A Hardware-Oriented Algorithm for Ultra-High-Speed Object Detection

Abstract

This paper describes a novel hardware-oriented algorithm that can be implemented on a field-programmable gate array in a high-speed vision platform for detection of multiple objects with clear texture information in images of $512\times 512$ pixels at 10 000 frames per second (fps) under complex background. The proposed algorithm is specially designed for devices with limited hardware resource for high-frame-rate, high-data-throughput, and high-parallelism processing of video streams with low latency. The proposed algorithm is based on the conventional histograms of oriented gradient (HOG) descriptor and support vector machine classifier algorithms. Considering the trade-off between speed and accuracy, many hardware-based optimization operations were implemented. The data throughput is nearly 29.30 Gbps while the latency for feature extraction is 0.76 us (61 clock period). After hardware-based image processing, the source image and the detected object features can be transferred to a personal computer for recording or post-processing at 10 000 fps. Several experiments were done to demonstrate the performance of our proposed algorithm for ultra-high-speed moving object detection with clear texture information in images.