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  Smart Video Surveillance Using YOLO Algorithm and Open CV  
  Authors : Sheetal Singh Bhandari; Shashank Dhoke; Omkar Sarang
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Due to object detection's close relationship with video analysis and image understanding, it has attracted much research attention in recent years. Traditional object detection methods are built on handcrafted features and shallow trainable architectures. Their performance easily stagnates by constructing complex ensembles that combine multiple low-level image features with highlevel context from object detectors and scene classifiers. With the rapid development in deep learning, more powerful tools, which can learn semantic, high-level, deeper features, are introduced to address the problems existing in traditional architectures. These models behave differently in network architecture, training strategy, and optimization function, etc. In this paper, we provide a review of deep learning-based object detection frameworks. Our review begins with a brief introduction to the history of deep learning and its representative tool, namely Convolutional Neural Network (CNN). Then we focus on typical generic object detection architectures along with some modifications and useful tricks to improve detection performance further. As distinct specific detection tasks exhibit different characteristics, we also briefly survey several specific tasks, including salient object detection, face detection, and pedestrian detection. Experimental analyses are also provided to compare various methods and draw some meaningful conclusions. Finally, several promising directions and tasks are provided to serve as guidelines for future work in both object detection and relevant neural network-based learning systems.


Published In : IJCSN Journal Volume 9, Issue 2

Date of Publication : April 2020

Pages : 93-100

Figures :06

Tables : --


Sheetal Singh Bhandari : Department of Information Technology, Excelsior Education Society's K.C. College of Engineering & Management Studies & Research, Thane, Maharashtra, India.

Shashank Dhoke : Department of Information Technology, Excelsior Education Society's K.C. College of Engineering & Management Studies & Research, Thane, Maharashtra, India.

Omkar Sarang : Department of Information Technology, Excelsior Education Society's K.C. College of Engineering & Management Studies & Research, Thane, Maharashtra, India.


Machine Learning, Neural networks, Convolutional Neural Network (CNN), YOLO Algorithm.

Thus, we have learned: . To create a system that will track and monitor the scene. . Detection of objects using trained models. . Real-time detection and recognition of any object instantly. . Includes facial detection and people counting where people tend to get lost easily.


[1] " Objects Talk - Object detection and Pattern Tracking using TensorFlow " University of Oulu, Degree Programme in Mathematical Sciences by P. Mustamo (2018). [2] "Crime Scene Prediction by Detecting Threatening Objects Using Convolutional Neural Network" by [3] "Detecting missing products in commercial refrigerators using convolutional neural networks" by [4] "Traffic Sign Detection and Recognition using a CNN Ensemble" by [5] "Efficient Detection of Patterns in 2D Trajectories of Moving Points" by Joachim Gudmundsson, Marc J. van Kreveld, BettinaSpeckmann(2007). [6] " The automatic detection of patterns in people's movements" by Gordon Forbes, GerharddeJager(2002). [7] "Object recognition in images using convolutional neural network" by Duth P. Sudharshan ,SwathiRaj [8] "Fast and Lightweight Object Detection Network: Detection and Recognition on Resource Constrained Devices" by BERNARDO AUGUSTO GODINHO DE OLIVEIRA,FLÁVIA MAGALHÃES FREITAS FERREIRA, AND CARLOS AUGUSTO PAIVADA- SILVA-MARTINS(2017). [9] ''Region lets for generic object detection,'' by Wang, M. Yang, S. Zhu, and Y. Lin, IEEE Trans. Pattern Anal. Mach. Intell., vol. 37, no. 10, pp. 2071-2084, Oct. 2015. [10] "Improvements of object detection using boosted histograms," by Laptev, in Procedure BMVC, vol.3., pp.949-958. (2006). [11] ''Text classification using WordNet hypernyms,'' by S. Scott and S. Matwin, in Proc. Conf. Use WordNet Natural Lang. Process. Syst., 1998, pp. 38-44. [12] ''Object recognition from local scale-invariant features,'' by D. G. Lowe, in Proc. IEEE Int. Conf. Compute. Vis., vol. 2. Sep. 1999, pp. 1150-1157. [13] ''Learning algorithm for non-linear support vector machines suited for digital VLSI,'' by D. Anguita, A. Boni, and S. Ridella, Electron. Lett., vol. 35, no. 16, pp. 1349-1350, Aug. 1999. [14] ''Architecting the next generation of service-based SCADA/DCS system of systems,'' by S. Karnouskos and A. W. Colombo, in Proc. 37th Annu. Conf. IEEE Ind. Electron. Soc. (IECON), Nov.2011, pp.359-364. [15] ''A performance study of general-purpose applications on graphics processors using Cuda,'' by S. Che, M. Boyer, J. Meng, D. Tarjan, J. W. Sheaffer, and K. Skadron, J. Parallel Distribution Compute., vol.68, no.10, pp.1370-1380,2008. [16] ''cu DNN: Efficient primitives for deep learning.'' by S. Chetluret al. (2014). [Online]. Available: https://arxiv.org/abs/1410.0759 [17] ''Image Net: A large-scale hierarchical image database,'' by J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, in Procedure IEEE Conf. Compute Vis. Pattern Recognition. (CVPR), Jun.2009, pp.248-255. [18] "Machine Learning: Parallel and Distributed Approaches." by R. Bekkerman, M. Bilenko, and J. Langford, Eds., Scaling up, Cambridge, U.K.: Cambridge Univ. Press, 2011. [19] "Object Detection with Deep Learning" A Review Zhong-Qiu Zhao, Member, IEEE, Peng Zheng, Shoutao Xu, and Xindong Wu, Fellow, IEEE.