Paper Info
Title
FPGA-based Accurate Pedestrian Detection with Thermal Camera for Surveillance System
Abstract
A surveillance system requires to achieve high accuracy of object detection at all times and to meet real-time processing requirements (30 frames per second) with high energy-efficiency. Since thermal cameras allow to see even in darkness unlike a RGB camera, object detection with a thermal camera obtains higher accuracy in the night, and thereby it has attracted much attention. However, since it is challenging to extract informative features from a thermal image, implementation challenges of an object detection with high accuracy remain. To meet the requirements, we present a sparse YOLOv2-based pedestrian detector with a thermal camera on an FPGA. For high accuracy, we propose a preprocessing that concatenates a thermal image with the background subtracted one for a detector to extract more informative features. Also, we develop a zero weight skipping architecture dedicated to our detector that contains a vectorizing unit that packs successive valid values into the same memory address to realize high parallel degree calculation. It leads to meet the real-time processing requirement with high energy-efficiency. Compared with a conventional one, F-score was 29 points higher, and speed was 3.3 times faster. Therefore, our system is more suitable for surveillance systems.
Year
DOI
Venue
2019
10.1109/ReConFig48160.2019.8994773
2019 International Conference on ReConFigurable Computing and FPGAs (ReConFig)
Keywords
Field
DocType
FPGA,Thermal Camera,YOLOv2,Pedestrian Detection
Object detection,Computer science,Field-programmable gate array,Real-time computing,Preprocessor,RGB color model,Frame rate,Memory address,Detector,Pedestrian detection
Conference
ISSN
ISBN
Citations 
2325-6532
978-1-7281-1958-8
0
PageRank 
References 
Authors
0.34
6
5
Name
Order
Citations
PageRank
Ryosuke Kuramochi102.70
Masayuki Shimoda286.45
Youki Sada311.71
Shimpei Sato44313.03
Hiroki Nakahara515537.34