A Review on Concept of Object Detection Techniques

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2019 by IJCTT Journal
Volume-67 Issue-8
Year of Publication : 2019
Authors : Rafah Amer Jaafar , Wurood A. Jbara , Shaymaa Adnan Abdulrahmana
DOI :  10.14445/22312803/IJCTT-V67I8P115

MLA

MLA Style:Rafah Amer Jaafar , Wurood A. Jbara , Shaymaa Adnan Abdulrahman"A Review on Concept of Object Detection Techniques" International Journal of Computer Trends and Technology 67.8 (2019):87-89.

APA Style Rafah Amer Jaafar , Wurood A. Jbara , Shaymaa Adnan Abdulrahman. A Review on Concept of Object Detection Techniques International Journal of Computer Trends and Technology, 67(8),87-89.

Abstract
Computer vision supply devices with capability to see the world around them visually, just like how humans utilizes their eyes. From several images can automatic extraction, analysis and understand of useful information. Object detection is a formula to see the computer that is gaining momentum in both technological communities and consumers. Object detection means classify and detect all objects in an image. Localization implies where the object is in an image and around it forms a square. Classification implies classify an image object from a set of predefined categories into a category. There are many of object detection techniques such as Background Subtraction, Template Matching, Shape Based and others. This paper present the concept of object detection , the researches interested in the field of object detection, difference between detection, localization and classification of objects, its importance and applications, General object detection framework ,the techniques used for object detection.

Reference
[1] Cyganek, Boguslaw. “Object detection and recognition in digital images: theory and practice”. John Wiley & Sons, 2013.
[2] [2] Li, Xiaobin, and Shengjin Wang. "Object detection using convolutional neural networks in a coarse-to-fine manner." IEEE Geoscience and Remote Sensing Letters 14.11 (2017): 2037-2041.
[3] Banerjee, Abhik, et al. "Mouse control using a web camera based on colour detection." International Journal of Computer Trends and Technology (IJCTT) – volume 9 number 1– Mar 2014.
[4] Zhang, Shifeng, et al. "Single-shot refinement neural network for object detection." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018.
[5] Sun, Xudong, Pengcheng Wu, and Steven CH Hoi. "Face detection using deep learning: An improved faster RCNN approach." Neurocomputing 299 (2018): 42-50.
[6] Hu, Han, et al. "Relation networks for object detection." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018.
[7] Liu, Yong, et al. "Structure inference net: Object detection using scene-level context and instance-level relationships." Proceedings of the IEEE conference on computer vision and pattern recognition. 2018.
[8] Zhao, Zhong-Qiu, et al. "Object detection with deep learning: A review." IEEE transactions on neural networks and learning systems (2019).
[9] Horprasert, Thanarat, David Harwood, and Larry S. Davis. "A statistical approach for real-time robust background subtraction and shadow detection." Ieee iccv. Vol. 99. No. 1999. Citeseer, 1999.
[10] Banharnsakun, Anan, and Supannee Tanathong. "Object detection based on template matching through use of bestso- far ABC." Computational intelligence and neuroscience 2014.
[11] Das, Deepjoy, and Dr Saharia. "Implementation and performance evaluation of background subtraction algorithms." arXiv preprint arXiv:1405.1815 (2014).
[12] Singh, Y. Jayanta, and Shalu Gupta. "Speedy object detection based on shape." arXiv preprint arXiv:1307.3439 (2013).
[13] Amit, Yali, and Pedro Felzenszwalb. "Object detection." Computer Vision: A Reference Guide (2014): 537-542.

Keywords
object detection, computer vision, classification, localization.