International Journal of Information and Communication Technology Research
مجله بین المللی ارتباطات و فناوری اطلاعات
International Journal of Information and Communication Technology Research
Engineering & Technology
http://ijict.itrc.ac.ir
1
admin
2251-6107
2783-4425
doi
1652
25391
en
jalali
1397
9
1
gregorian
2018
12
1
10
4
online
1
fulltext
other
Deep Learning Based on Parallel CNNs for Pedestrian Detection
فناوری اطلاعات
Information Technology
پژوهشي
Research
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.
Parallel DCNN, Pedestrian Detection, Region-based Convolutional Neural Network (RCNN), Single Shot Detector (SSD), Training Approach.
42
52
http://ijict.itrc.ac.ir/browse.php?a_code=A-10-4232-3&slc_lang=other&sid=1
Mahmoud
Saeidi
msaeidi40@itrc.ac.ir
10031947532846001319
10031947532846001319
Yes
Faculty of Computer Engineering K. N. Toosi University of Technology Tehran, Iran
Ali
Ahmadi
ahmadi@eetd.kntu.ac.ir
10031947532846001320
10031947532846001320
No
Faculty of Computer Engineering K. N. Toosi University of Technology Tehran, Iran