Smart City Gnosys

Smart city article details

Title	The Object Detection Method For Pedestrian Video Based On Improved Yolov3
ID_Doc	56151
Authors	Feng Z.; You F.
Year	2021
Published	ACM International Conference Proceeding Series
DOI	http://dx.doi.org/10.1145/3495018.3495116
Abstract	With the development of smart city and the popularization and application of video surveillance, pedestrian target detection has played a more and more important role in real life. Compared with the traditional pedestrian detection algorithm, the pedestrian target detection technology based on deep learning has higher detection accuracy, however, there are still some problems in the current target detection algorithms, such as the inaccurate location of the target, the more missed detection and the unimproved detection accuracy. First of all, in the image division size, this paper finally selected 10×10 as the image division size. Secondly, this paper improves the K-MEANS clustering algorithm by adding data filtering process before clustering to make the initial size of the network candidate box more fit to the characteristics of pedestrian targets. Finally, this paper compares the improved detection module's YOLOv3 with the original YOLOv3 on the VOC Data to verify the generality of this improvement. © 2021 ACM.
Author Keywords	Convolution neural network; Deep Learning; Image Process; K-means Clustering; Object Detection; YOLOv3

Similar Articles

Id	Similarity	Authors	Title	Published
41532	0.907	Zhang J.; Chen X.; Li Y.; Chen T.; Mou L.	Pedestrian Detection Algorithm Based On Improved Yolo V3	2021 IEEE International Conference on Power, Intelligent Computing and Systems, ICPICS 2021 (2021)
62125	0.885	Song F.; Li P.	Yolov5-Ms: Real-Time Multi-Surveillance Pedestrian Target Detection Model For Smart Cities	Biomimetics, 8, 6 (2023)
44268	0.881	Murthy C.B.; Hashmi M.F.	Real Time Pedestrian Detection Using Robust Enhanced Yolov3+	Proceedings - 2020 21st International Arab Conference on Information Technology, ACIT 2020 (2020)
23647	0.875	Bhonde T.; Temare H.; Dadwhal Y.S.	Enhanced Object Detection Using Yolov8: Identifying Vehicles And Pedestrians In Urban Environments	2024 IEEE Pune Section International Conference, PuneCon 2024 (2024)
14521	0.874	Yuan G.; Ye T.; Fu H.; Wang L.; Wang Z.	Clustering Based Detection Of Small Target Pedestrians For Smart Cities	Sustainable Energy Technologies and Assessments, 52 (2022)
44267	0.873	Murthy C.B.; Farukh Hashmi M.	Real Time Pedestrian Detection Using Robust Enhanced Tiny-Yolov3	2020 IEEE 17th India Council International Conference, INDICON 2020 (2020)
45926	0.87	Guo R.; Li S.; Wang K.	Research On Yolov3 Algorithm Based On Darknet Framework	Journal of Physics: Conference Series, 1629, 1 (2020)
15079	0.862	Khalfaoui A.; Badri A.; Mourabit I.E.L.	Comparative Study Of Yolov3 And Yolov5'S Performances For Real-Time Person Detection	2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology, IRASET 2022 (2022)
60908	0.859	Zhao S.; You F.	Vehicle Detection Based On Improved Yolov3 Algorithm	Proceedings - 2020 International Conference on Intelligent Transportation, Big Data and Smart City, ICITBS 2020 (2020)
41534	0.857	Xie C.; Li P.; Sun Y.	Pedestrian Detection And Location Algorithm Based On Deep Learning	Proceedings - 2019 International Conference on Intelligent Transportation, Big Data and Smart City, ICITBS 2019 (2019)