Affiliation:
1. Christu Jyothi Institute of Technology & Science, Jangaon, Telangana, India
Abstract
During the last years, a noticeable growth is observed in the field of computer vision research. In computer vision, object detection is a task of classifying and localizing the objects in order to detect the same. The widely used object detection applications are human– computer interaction, video surveillance, satellite imagery, transport system, and activity recognition. In the wider family of deep learning architectures, convolutional neural network (CNN) made up with set of neural network layers is used for visual imagery. Deep CNN architectures exhibit impressive results for detection of objects in digital image. This paper represents a comprehensive review of the recent development in object detection using convolutional neural networks
Reference15 articles.
1. [1]V. Gajjar, A. Gurnani and Y. Khandhediya, "Human Detection and Tracking for Video Surveillance: A Cognitive Science Approach," in 2017 IEEE International Conference on Computer Vision Workshops, 2017.
2. [2]M. Adel, A. Moussaoui, M. Rasigni, S. Bourennane and L. Hamami, "Statistical-Based Tracking Technique for Linear Structures Detection: Application to Vessel Segmentation in Medical Images," IEEE Signal Processing Letters, vol. 17, no. 6, pp. 555-558, June 2010.
3. [3]X.-T. Truong, V. N. Yoong and T.-D. Ngo, "RGB-D and Laser Data Fusion-based Human Detection and Tracking for Socially Aware Robot Navigation Framework," in IEEE Conference on Robotics and Biomimetics, Zhuhai, China, 2015.
4. [4]H. S. Parekh, D. G. Thakore and U. K. Jaliya, "A Survey on Object Detection and Tracking Methods," International Journal of Innovative Research in Computer and Communication Engineering, vol. 2, no. 2, pp. 2970-2978, February 2014.
5. [5]A. Krizhevsky, I. Sutskever and G. E. Hinton, "ImageNet Classification with Deep Convolutional Neural Networks," in Advances in neural information processing systems, 2012.