Total views : 226
A Hybrid Approach for Real-Time Object Detection and Tracking to Cover Background Turbulence Problem
Objectives: A number of techniques available in literature do not discuss the problem of incorrect object detection in the scenes having unstable or moving background. A novel hybrid technique is proposed in the paper to cover this problem. Methods/Statistical Analysis: The new approach proposed is the hybrid of two well known techniques for object detection. One is frame-differencing approach and other is skin colour modelling. This newer technique exploits the fact that the demerit of one technique works as merit for other and hence hybrid technique resolves the problem of moving background or turbulence in background. A real time video having turbulence in background is used for testing the efficiency of the approach. Findings: With accuracy of 0.97, the proposed approach outperforms the individual approaches i.e. frame-differencing and skin colour modelling. A very low value of False Positive Rate (FPR) for proposed approach compared to other approaches confirms the least incorrect detections. High value of True Positive Rate (TPR) conveys that fall out of correct object is least in the video by proposed approach. Results show that the proposed approach better applicable for background with turbulence. Application/Improvements: Automatic object detection and tracking in applications as surveillance are tractable with the proposed hybrid approach.
Frame-Differencing, Object Detection, Object Tracking, Skin Colour, Thresholding.
- Power PW, Schoonees JA. Understanding background mixture models for foreground segmentation. Proceedings Image and Vision Computing; New Zealand. 2002. p. 267–71.
- Xiong W. Moving object detection algorithm based on background subtraction and frame differencing. Proceedings of IEEE 30th Chinese Control Conference (CCC); China. 2011. p. 3273–6.
- Rita C. Improving shadow suppression in moving object detection with HSV color information. Proceedings of Intelligent Transportation Systems; Oakland Calif. 2001. p. 334–9.
- Marko H, Pietikainen M. A texture-based method for modelling the background and detecting moving objects. IEEE Transactions on Pattern Analysis and Machine Intelligence. USA. 2006:28(4):657–62.
- Jianhua Y, Gao T, Zhang J. Moving object detection with background subtraction and shadow removal. Proceedings of 9th International Conference on Fuzzy Systems and Knowledge Discovery); China. 2012.
- Jianxin W. C4: A real-time object detection framework. IEEE Transactions on Image Processing. 2013; 22(10):4096–107.
- Paul V, Jones M. Robust real-time object detection. International Journal of Computer Vision. 2001; 4:51–2.
- Navneet D, Triggs B. Histograms of oriented gradients for human detection. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. USA. 2005; 1(1):886–93.
- Piotr D. Pedestrian detection: A benchmark. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition; USA. 2009. p. 1–8.
- Jianxin W. Fast asymmetric learning for cascade face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2008; 30(3):369–82.
- Jianxin W, Geyer C, Rehg JM. Real-time human detection using contour cues. Proceedings of IEEE Conference on Robotics and Automation; Shanghai, China. 2011. p. 860–7.
- Intan K, Mohamed SS. Frame differencing with post-processing techniques for moving object detection in outdoor environment. Proceedings of IEEE 7th International Colloquium on Signal Processing and its Applications. 2011; 2(1):263–6.
- Ramprasad P, Nelson R. Low level recognition of human motion (or how to get your man without finding his body parts). Proceedings of the IEEE Workshop on Motion of Non-Rigid and Articulated Objects; USA. 1994.
- Richard WC. Pfinder: Real-time tracking of the human body. IEEE Transactions on Pattern Analysis and Machine Intelligence.1997; 19(7):780–5.
- Chris S, Grimson ELW. Adaptive background mixture models for real-time tracking. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition; Cambridge. 1999. p. 1–7.
- Marko H, Pietikainen M. A texture-based method for modelling the background and detecting moving objects. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2006; 28(4):657–62.
- Rosito JC. Efficient background subtraction and shadow removal for monochromatic video sequences. IEEE Transactions on Multimedia. 2009; 11(3):571–7.
- Alan JL, Fujiyoshi H, Patil RS. Moving target classification and tracking from real-time video. Proceedings of Fourth IEEE Workshop on Applications of Computer Vision WACV'98; 1998.
- Budi S. Tracking of moving objects by using a low resolution image. Proceedings of Second IEEE International Conference on Innovative Computing, Information and Control, ICICIC'07; 2007.
- Vezhnevets V, Sazonov V, Andreeva A. A survey on pixel-based skin color detection techniques. Proceedings of Graphicon; 2003.
- Lam PS, Bouzerdoum A, Chai D. A novel skin color model in ycbcr color space and its application to human face detection. Proceedings of IEEE International Conference on. Image Processing; 2002.
- Douglas C, Ngan KN. Face segmentation using skin-color map in videophone applications. IEEE Transactions on Circuits and Systems for Video Technology; 1999. p. 551–64.
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.