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Reconfigurable Pedestrian Detection System Using Deep Learning for Video Surveillance

M.K. JEEVARAJAN, P. NIRMAL KUMAR

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Summary :

We present a reconfigurable deep learning pedestrian detection system for surveillance systems that detect people with shadows in different lighting and heavily occluded conditions. This work proposes a region-based CNN, combined with CMOS and thermal cameras to obtain human features even under poor lighting conditions. The main advantage of a reconfigurable system with respect to processor-based systems is its high performance and parallelism when processing large amount of data such as video frames. We discuss the details of hardware implementation in the proposed real-time pedestrian detection algorithm on a Zynq FPGA. Simulation results show that the proposed integrated approach of R-CNN architecture with cameras provides better performance in terms of accuracy, precision, and F1-score. The performance of Zynq FPGA was compared to other works, which showed that the proposed architecture is a good trade-off in terms of quality, accuracy, speed, and resource utilization.

Publication
IEICE TRANSACTIONS on Information Vol.E106-D No.9 pp.1610-1614
Publication Date
2023/09/01
Publicized
2023/06/09
Online ISSN
1745-1361
DOI
10.1587/transinf.2019EDL8132
Type of Manuscript
LETTER
Category
Image Processing and Video Processing

Authors

M.K. JEEVARAJAN
  Anna University
P. NIRMAL KUMAR
  Anna University

Keyword