Abstract

Autonomous vehicles, surveillance systems, face detection systems lead to the development of accurate object detection system [1]. These systems recognize, classify and localize every object in an image by drawing bounding boxes around the object [2]. These systems use existing classification models as backbone for Object Detection purpose. Object detection is the process of finding instances of real-world objects such as human faces, animals and vehicles etc., in pictures, images or in videos. An Object detection algorithm uses extracted features and learning techniques to recognize the objects in an image. In this paper, various Object Detection techniques have been studied and some of them are implemented. As a part of this paper, three algorithms for object detection in an image were implemented and their results were compared. The algorithms are “Object Detection using Deep Learning Framework by OpenCV”, “Object Detection using Tensorflow” and “Object Detection using Keras models”.

References

• Mohannad Elhamod, Martin D. Levine, Automated Real-Time Detection of Potentially Suspicious Behaviour in Public Transport Areas. In IEEE Transactions on Intelligent Transportation systems, vol. 14, no. 2, June 2013
• Ajeet Ram Pathak, Manjusha Pandey, Siddharth Rautaray, Application of Deep Learning for Object Detection. In International Conference on Computaional Intelligence and Data Science(ICCIDS) may 2018 pp.1-11.
• Christian Szegedy, Alexander Toshev, Dumitru Erhan, Deep Neural Networks for Object Detection. In International conference on neural information processing systems 2013.
• Xiaofeng Ning, Wen Zhu , Shifeng Chen, Recognition, Object Detection and Segmentation of White Background Photos Based on Deep Learning. In Youth Academic Annual Conference of Chinese Association of Automation (YAC) May 2017 pp. 1-6.
• Zhong-Qiu Zhao, Peng Zheng, Shou-tao Xu, Xindong Wu, Object Detection with Deep Learning: A Review. In Journal of Latex Class Files, Vol. 14, no. 8, March 2017
• Sakshi Indolia, Anil Kumar Goswani, S. P. Mishra, Pooja Asopa, Conceptual Understanding of Convolutional Neural Network- A Deep Learning Appraoch. In International Conference on Computational Intelligence and Data Sience(ICCIDS) 2018 pp.1-10.
• Yann LeCun, Yoshua Bengio , Geoffery Hinton, Deep Learning. In Review, vol. 521, May 2015.
• Ross Girshick, Fast R-CNN. In IEEE International Conference on Computer Vision December 2015 pp. 1-9.
• Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi, You Only Look Once: Unified, Real-Time Object Detection. In IEEE Conference on Computer Vision and Pattern Recognition june 2016 pp. 1-10.
• Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, Trevor Darrell, Convolutional architecture for fast feature embedding. In ACM international conference on Multimedia Nov 2014 pp. 1-4.
• Maarten C. Kruithof, Henri Bouma, Noelle M. Fischer Klamer Schutte, Object recognition using deep convolutional neural networks with complete transfer and partial frozen layers. In SPIE Security+ Defense Conference Oct 2016 pp. 1-8.