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A Systematic Deep Learning Based Overhead Tracking and Counting System Using RGB-D Remote Cameras

Title
A Systematic Deep Learning Based Overhead Tracking and Counting System Using RGB-D Remote Cameras
Author
김기범
Keywords
Apriori-Association; Cross-line judgment; deep learning; head tracking; Hough Circular Gradient Transform; Fused intra-inter trajectories
Issue Date
2021-06
Publisher
MDPI
Citation
APPLIED SCIENCES-BASEL, v. 11, no. 12, Article no. 5503, 21pp
Abstract
Featured Application The proposed technique is an application for people detection and counting which is evaluated over several challenging benchmark datasets. The technique can be applied in heavy crowd assistance systems that help to find targeted persons, to track functional movements and to maximize the performance of surveillance security. Automatic head tracking and counting using depth imagery has various practical applications in security, logistics, queue management, space utilization and visitor counting. However, no currently available system can clearly distinguish between a human head and other objects in order to track and count people accurately. For this reason, we propose a novel system that can track people by monitoring their heads and shoulders in complex environments and also count the number of people entering and exiting the scene. Our system is split into six phases at first, preprocessing is done by converting videos of a scene into frames and removing the background from the video frames. Second, heads are detected using Hough Circular Gradient Transform, and shoulders are detected by HOG based symmetry methods. Third, three robust features, namely, fused joint HOG-LBP, Energy based Point clouds and Fused intra-inter trajectories are extracted. Fourth, the Apriori-Association is implemented to select the best features. Fifth, deep learning is used for accurate people tracking. Finally, heads are counted using Cross-line judgment. The system was tested on three benchmark datasets: the PCDS dataset, the MICC people counting dataset and the GOTPD dataset and counting accuracy of 98.40%, 98%, and 99% respectively was achieved. Our system obtained remarkable results.
URI
https://www.proquest.com/docview/2544958318?accountid=11283https://repository.hanyang.ac.kr/handle/20.500.11754/166594
ISSN
2076-3417
DOI
10.3390/app11125503
Appears in Collections:
ETC[S] > 연구정보
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