Paper Info
Title
Parallel Implementation Strategy For Cohog-Based Pedestrian Detection Using A Multi-Core Processor
Abstract
Pedestrian detection from visual images, which is used for driver assistance or video surveillance, is a recent challenging problem. Co-occurrence histograms of oriented gradients (CoHOG) is a powerful feature descriptor for pedestrian detection and achieves the highest detection accuracy. However, its calculation cost is too large to calculate it in real-time on state-of-the-art processors. In this paper, to obtain optimal parallel implementation for an NVIDIA GPU, several kinds of parallelism of CoHOG-based detection are shown and evaluated suitability for implementation. The experimental result shows that the detection process can be performed at 16.5 fps in QVGA images on NVIDIA Testa C1060 by optimized parallel implementation. By our evaluation, it is shown that the optimal strategy of parallel implementation for an NVIDIA GPU is different from that of FPGA. We discuss about the reason and show the advantages of each device. To show the scalability and portability of GPU implementation, the same object code is executed on other NVIDA GPUs. The experimental result shows that GTX570 can perform the CoHOG-based pedestiran detection 21.3 fps in QVGA images.
Year
DOI
Venue
2011
10.1587/transfun.E94.A.2315
IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES
Keywords
Field
DocType
pedestrian detection, parallel implementation, CoHOG, GPU computing
Computer architecture,Parallel computing,General-purpose computing on graphics processing units,Multi-core processor,Pedestrian detection,Mathematics
Journal
Volume
Issue
ISSN
E94A
11
0916-8508
Citations 
PageRank 
References 
4
0.48
21
Authors
2
Name
Order
Citations
PageRank
Ryusuke Miyamoto116315.01
Hiroki Sugano214111.19