Abstract
Surveillance system has proven as a key step in providing public security in many crowded places like railway stations, bus stops, cinemas, malls, etc. Several advancements in computer vision have been found but very less is applied in actual implementation of surveillance system. There is a need to add some intelligence in the surveillance system which can accurately detect human objects. This paper presents a method for human object detection using stationary wavelet transform (SWT) coefficients. Stationary wavelet transform coefficients are independent to the other parameters like color, shape, size, etc., of the object. The proposed method detects the human object from the complex images. The use of shift-invariance property of stationary wavelet transform handles object translations well. The detection of human object has been performed using Adaboost classifier. The quantitative assessments of the proposed method have shown improved performance over other state-of-the-art methods.
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References
Hu, W., Tan, T.: A survey on visual surveillance of object motion and behaviors. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 34(3), 334–352 (2006)
Brooks, R.R., Grewe, L., Iyengar, S.S.: Recognition in wavelet domain: a survey. J. Electron. Imaging 10(3), 757–784 (2001)
Sialat, M., Khlifat, N., Bremond, F., Hamrouni, K.: People detection in complex scene using a cascade of boosted classifiers based on Haar-like features. In: Proceeding of IEEE International Symposium on Intelligent Vehicles, pp. 83–87 (2009)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceeding of IEEE International Conference on Computer vision and Pattern Recognition, vol. 1, pp. 83–87 (2001)
Renno, J.P., Makris, D., Jones, G.A.: Object classification in visual surveillance using Adaboost. In: Proceeding of IEEE International conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–8 (2007)
Lowe, D.: Object recognition from local scale invariant features. In: Proceeding of 7th IEEE International Conference on Computer Vision, pp. 1150–1157 (1999)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceeding of IEEE International Conference on Computer vision and Pattern Recognition, pp. 886–893 (2005)
Cao, X., Wu, C., Yan, P., Li, X.: Linear SVM classification using boosting HoG Features for vehicle detection in low-altitude airborne videos. In: Proceeding of IEEE International Conference on Image Processing, pp. 2421–2424 (2011)
Lu, H., Zheng, Z.: Two novel real-time local visual features for omni directional vision. Pattern Recogn. 43(12), 3938–3949 (2010)
Chen, L., Feris, R., Zhai, Y., Brown, L., Hampapur, A.: An integrated system for moving object classification in surveillance videos. In: Proceeding of IEEE International conference on Advanced Video and Signal based Surveillance, pp. 52–59 (2008)
Nason, G.P., Silver, B.W.: The stationary wavelet transform and some statistical applications. Technical. report BS8 1Tw, University of Bristol (1995)
INRIA Person dataset. http://pascal.inrialpes.fr/data/human (2014). Accessed 23 Aug 2014
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Om Prakash, Manish Khare, Binh, N.T., Ashish Khare (2016). Human Object Detection in Images Using Shift-Invariant Stationary Wavelet Transform. In: Afzalpulkar, N., Srivastava, V., Singh, G., Bhatnagar, D. (eds) Proceedings of the International Conference on Recent Cognizance in Wireless Communication & Image Processing. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2638-3_55
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DOI: https://doi.org/10.1007/978-81-322-2638-3_55
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