Volume 10, Issue 4 (12-2018)                   IJICTR 2018, 10(4): 42-52 | Back to browse issues page

XML Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Saeidi M, Ahmadi A. Deep Learning Based on Parallel CNNs for Pedestrian Detection. IJICTR. 2018; 10 (4) :42-52
URL: http://ijict.itrc.ac.ir/article-1-410-en.html
1- Faculty of Computer Engineering K. N. Toosi University of Technology Tehran, Iran , msaeidi40@itrc.ac.ir
2- Faculty of Computer Engineering K. N. Toosi University of Technology Tehran, Iran
Abstract:   (2031 Views)
Recently, deep learning methods, mostly algorithms based on Deep Convolutional Neural Networks (DCNNs) have yielded great results on pedestrian detection. Algorithms based on DCNNs spontaneously learn features in a supervised manner and are able to learn qualified high level feature representations to detect pedestrian. In this paper, we first review a number of popular DCNN-based training approaches along with their recent extensions. We then briefly describe recent algorithms based on these approaches. Also, we accentuate recent contributions and main challenges of DCNNs in detecting pedestrian. We analyze deep pedestrian detection algorithms from training approach, categorization, and DCNN model points of view, and ultimately propose a new deep architecture and training approach for deep pedestrian detection. The experimental results show that the proposed DCNN and training approach, achieve more accurate rate detection than the previously reported architectures and training approaches.
Full-Text [PDF 1042 kb]   (1388 Downloads)    
Type of Study: Research | Subject: Information Technology

Add your comments about this article : Your username or Email:

Send email to the article author