Abstract
The paper proposed a framework to design and develop object detection and tracking using learning based algorithms. It is an important event in security and surveillance systems using images or videos. YOLO V3 model is used for object detection and is trained using coco dataset. The objects detected are Humans, bottles, Drilling machine, Air powered saw, etc. The model detect the objects from camera field view, i.e. objects that are clearly viewed. The proposed model with pre-processing, provides an average accuracy of around 96% with different test cases.
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References
J. Redmon, et al., You only look once: unified, real-time object detection, in The IEEE Conference on CVPR (2016 ), pp. 779–788
S. Wang, et al., Research on image recognition of insulators based on YOLO algorithm, in International Conference on PST, pp. 3871–3874 (2018)
A.P. Jana, et al. YOLO based detection and classification of objects in video records, in 3rd International Conference on RTEICT, India (2018), pp. 2448–2452
J. Redmon, et al., The IEEE Conference on CVPR, pp. 7263–7271, 2017
M. Buric, et al. Ball detection using yolo and mask R-CNN, International Conference on CSCI, USA, pp. 319–323
W. Lan, et al. Pedestrian detection based on YOLO network model, in ICMA, pp. 1547–1551 (2018)
C.S. Asha, et al. Vehicle counting for traffic management system using YOLO and correlation filter, in IEEE ICECCT (Bangalore, 2018) pp. 1–6
Q. Peng, et al. Pedestrian detection for transformer substation based on GMM and YOLO, in 8th International Conference on IHMSC, pp. 562–565 (2016)
K. Jo, et al. A real-time multi-class multi-object tracker using YOLOv2, in 2017 IEEE International Conference on SIPA (Kuching, 2017), pp. 507–511
J. Zhang, et al. A real-time Chinese traffic sign detection algorithm based on modified YOLOv2 Algorithms 10 4, 127 (2017)
J. Redmon, A. Farhadi, The IEEE Conference CVPR, pp. 7263–7271 (2017)
R. Girshick, et al. The IEEE Conference on CVPR, pp. 580–587 (2014)
J.R.R. Uijlings et al., Selective search for object recognition. Int. J. Comput. Vis. 104, 154–171 (2013)
S. Pavaskar et al., Real-time vehicle-type categorization and character extraction from the license plates. CISC 557–565 (2019)
V.B. Suneeta, P. Purushottam, K. Prashantkumar, S. Sachin, M. Supreet, Facial expression recognition using supervised learning, in Computational Vision and Bio-Inspired Computing, ed. by S. Smys, J. Tavares, V. Balas, A. Iliyasu. ICCVBIC 2019. Advances in Intelligent Systems and Computing, vol 1108 (Springer, Cham, 2020). https://doi.org/10.1007/978-3-030-37218-7_32
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Burde, S., Budihal, S.V. (2021). Multiple Object Detection and Tracking Using Deep Learning. In: Sabut, S.K., Ray, A.K., Pati, B., Acharya, U.R. (eds) Proceedings of International Conference on Communication, Circuits, and Systems. Lecture Notes in Electrical Engineering, vol 728. Springer, Singapore. https://doi.org/10.1007/978-981-33-4866-0_32
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DOI: https://doi.org/10.1007/978-981-33-4866-0_32
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