TY - GEN
T1 - An Efficient Multi-Object Tracking and Counting Framework Using Video Streaming in Urban Vehicular Environments
AU - Dirir, Ahmed
AU - Adib, Mohammed
AU - Mahmoud, Anas
AU - Al-Gunaid, Moatasem
AU - El-Sayed, Hesham
N1 - Funding Information:
This research was supported by the Research Office at the United Arab Emirates University (grant number G00003133) and the Roadway, Transportation, and Traffic Safety Research Center (RTTSRC) of the United Arab Emirates University (grant number 31R116).
Publisher Copyright:
© 2020 IEEE
PY - 2021/3/16
Y1 - 2021/3/16
N2 - Object counting is an active research area that gained more attention in the last few years. Since deep learning methods outperformed all other object detection algorithms, the design of efficient object counting algorithms became more realistic and achievable. Numerous algorithms targeting various challenges associated with object counting have been introduced. In a smart transportation system, vehicle counting plays a crucial role as it helps in creating autonomous systems, and better planning for roads. In this paper, we present an efficient object counting system and assess its performance using a dataset of 20 different videos. The proposed system leverage an efficient object detector, and object tracker to perform the counting. This paper combines different approaches to count objects by tracking them, but performs the tracking operation efficiently. Therefore, the proposed systems achieve high accuracy values with low processing time.
AB - Object counting is an active research area that gained more attention in the last few years. Since deep learning methods outperformed all other object detection algorithms, the design of efficient object counting algorithms became more realistic and achievable. Numerous algorithms targeting various challenges associated with object counting have been introduced. In a smart transportation system, vehicle counting plays a crucial role as it helps in creating autonomous systems, and better planning for roads. In this paper, we present an efficient object counting system and assess its performance using a dataset of 20 different videos. The proposed system leverage an efficient object detector, and object tracker to perform the counting. This paper combines different approaches to count objects by tracking them, but performs the tracking operation efficiently. Therefore, the proposed systems achieve high accuracy values with low processing time.
KW - Counting
KW - KCF
KW - Object Detection & Tracking
KW - YOLOv2
UR - http://www.scopus.com/inward/record.url?scp=85120795619&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85120795619&partnerID=8YFLogxK
U2 - 10.1109/ICCSPA49915.2021.9385732
DO - 10.1109/ICCSPA49915.2021.9385732
M3 - Conference contribution
AN - SCOPUS:85120795619
T3 - ICCSPA 2020 - 4th International Conference on Communications, Signal Processing, and their Applications
BT - ICCSPA 2020 - 4th International Conference on Communications, Signal Processing, and their Applications
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th International Conference on Communications, Signal Processing, and their Applications, ICCSPA 2020
Y2 - 16 March 2021 through 18 March 2021
ER -