The week's pick
Random Articles
Reseach Article
A Study on Computer Vision Systems for Real-Time Object Detection and Tracking
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 175 - Number 3 |
| Year of Publication: 2017 |
| Authors: Daniel Mohammed, Francis A. Amavi |
10.5120/ijca2017915484
|
Daniel Mohammed, Francis A. Amavi . A Study on Computer Vision Systems for Real-Time Object Detection and Tracking. International Journal of Computer Applications. 175, 3 ( Oct 2017), 24-27. DOI=10.5120/ijca2017915484
Abstract
Computer Vision (CV) concentrates on the automatic extraction, examination and comprehension of valuable data from a solitary image or a group of images. Object tracking, one of the key areas in CV has received a lot of attenstion in recent times. Tracking objects is a systematic process of monitoring the movement of a target object from its initial state to the nth state over a period of time using a camera. This technique is usually employed as a security feature in both military and civilian systems. However, prior studies has shown that tracking objects in motion is a very difficult task and is a hot research hotspot in the field of computer vision and machine learning. In this review paper we discuess various techniques in detection, tracking and some other related works of moving objects in video streams.
References
- Rout, R.K., A survey on object detection and tracking algorithms. 2013.
- Parekh, H.S., 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, 2014. 2(2): p. 2970-2979.
- Chen, Y.-L., et al., A real-time vision system for nighttime vehicle detection and traffic surveillance. IEEE Transactions on Industrial Electronics, 2011. 58(5): p. 2030-2044.
- P, P.R.K., Detecting Abandon Objects Fastly Through Blob Analysis. IJREAT International Journal of Research in Engineering & Advanced Technology, , 2014. Volume 2(Issue 2).
- Gyaourova, A., C. Kamath, and S. Cheung, Block matching for object tracking. Lawrence livermore national laboratory, 2003.
- Yilmaz, A., O. Javed, and M. Shah, Object tracking: A survey. Acm computing surveys (CSUR), 2006. 38(4): p. 13.
- Dedeoglu, Y., Moving object detection, tracking and classification for smart video surveillance. 2004, bilkent university.
- Ranipa, K.R. and K. Bhatt, Illumination Condition Effect on Object Tracking: A Review. Global Journal of Computer Science and Technology, 2014. 14(5-F): p. 9.
- Arbeláez, P., et al. Semantic segmentation using regions and parts. in Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on. 2012. IEEE.
- Chen, X., et al. Detect what you can: Detecting and representing objects using holistic models and body parts. in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2014.
- Sukanya, C., R. Gokul, and V. Paul, A Survey on Object Recognition Methods. International Journal of Science, Engineering and Computer Technology, 2016. 6(1): p. 48.
- Alex, D.S. and A. WAHI, BSFD: Background Subtraction Frame Difference Algorithm For Moving Object Detection And Extraction. Journal of Theoretical & Applied Information Technology, 2014. 60(3).
- Singla, N., Motion detection based on frame difference method. International Journal of Information & Computation Technology, 2014. 4(15): p. 1559-1565.
- Helgesen, H.H., Object detection and tracking based on optical flow in unmanned aerial vehicles. 2015, NTNU.
- Joshi, U. and K. Patel, Object tracking and classification under illumination variations. 2016.
- Roberts, W., et al. Vehicle tracking for urban surveillance. in SPIE Defense and Security Symposium. 2008. International Society for Optics and Photonics.
- Gould, S., T. Gao, and D. Koller. Region-based segmentation and object detection. in Advances in neural information processing systems. 2009.
- Haritaoglu, I., D. Harwood, and L.S. Davis, W 4: Real-time surveillance of people and their activities. IEEE Transactions on pattern analysis and machine intelligence, 2000. 22(8): p. 809-830.
- Zhang, R. and J. Ding, Object tracking and detecting based on adaptive background subtraction. Procedia Engineering, 2012. 29: p. 1351-1355.
- Pentenrieder, K., P. Meier, and G. Klinker. Analysis of tracking accuracy for single-camera square-marker-based tracking. in Proc. Dritter Workshop Virtuelle und Erweiterte Realitt der GIFachgruppe VR/AR, Koblenz, Germany. 2006.
- Mishra, P.K. and G. Saroha, A Study on Classification for Static and Moving Object in Video Surveillance System. 2016.
- Fernandez, J.-C., L. Mounier, and C. Pachon. A model-based approach for robustness testing. in IFIP International Conference on Testing of Communicating Systems. 2005. Springer.
- Szeliski, R., Computer vision: algorithms and applications. 2010: Springer Science & Business Media.
- Bhattacharya, S., et al., Moving object detection and tracking in forward looking infra-red aerial imagery, in Machine Vision Beyond Visible Spectrum. 2011, Springer. p. 221-252.
Index Terms
Keywords