© The Institution of Engineering and Technology
Moving object detection in video streams is a challenging and integral part of computer vision which is used in surveillance, traffic and site monitoring, and navigation. Compared with the background-based techniques, frame differencing technique is computationally inexpensive. However, frame differencing technique only detects the boundary of a moving object. Due to changing light conditions, shadows, poor contrast between object and background, and a slow-moving object, object detection rate from frame differencing technique reduces. This is because the number of noisy frames and frames with missing/partially detected object increases. Application of large kernel size morphological operations fails to remove noise as they might remove the boundary (or part) of a moving object. In this study, the authors propose a methodology to improve the frame differencing technique using footstep sound generated by a moving object. Audio recorded with the video system is processed and footstep sound is detected using audio features computed as mel-frequency cepstral coefficients. Number of frames within each footstep sound are counted and processed. Spatial segmentation is used to find the moving object in noisy frames. A missing or partially detected object is recovered by modelling an ellipse using a moving object from other neighbourhood frames.
References
-
-
1)
-
21. Heikkila, M., Pietikainen, M.: ‘A texture-based method for modeling the background and detecting moving objects’, IEEE Trans. Pattern Anal. Mach. Intell., 2006, 28, (4), pp. 657–662.
-
2)
-
6. Cui, Y., Zeng, Z., Cui, W., et al: ‘Moving object detection based frame difference and graph cuts’, J. Comput. Inf. Syst., 2012, 8, (1), pp. 21–29.
-
3)
-
4)
-
25. Han, J., Bhanu, B.: ‘Fusion of color and infrared video for moving human detection’, Pattern Recognit., 2007, 40, (6), pp. 1771–1784.
-
5)
-
20. Zhou, D., Zhang, H.: ‘Modified GMM background modeling and optical flow for detection of moving objects’. IEEE Int. Conf. on Systems, Man and Cybernetics, 2005, , pp. 2224–2229.
-
6)
-
7)
-
22. Doukas, C., Maglogiannis, I.: ‘Advanced patient or elder fall detection based on movement and sound data’. Second Int. Conf. on Pervasive Computing Technologies for Healthcare, January 2008, pp. 103–107.
-
8)
-
31. Oppenheim, A.V., Ronald, W.S., John, R.B.: ‘Discrete-time signal processing’ (Prentice-Hall, Upper Saddle River, NJ, 1999).
-
9)
-
13. Piccardi, M.: ‘Background subtraction techniques: a review’. 2004 IEEE Int. Conf. on Systems, Man and Cybernetics, 10–13 October 2004, pp. 3099–3104.
-
10)
-
11)
-
1. Kumar, P., Singhal, A., Mehta, S., et al: ‘Real-time moving object detection algorithm on high-resolution videos using GPUs’, Real-Time Image Process., 2016, 11, (1), pp. 93–109.
-
12)
-
2. Kamate, S., Yilmazer, N.: ‘Application of object detection and tracking techniques for unmanned aerial vehicles’, Procedia Comput. Sci., 2015, 61, pp. 436–441.
-
13)
-
8. Shafie, A.A., Hafiz, F., Ali, M.H.: ‘Motion detection techniques using optical flow’, Int. J. Electr. Comput. Energ. Electron. Commun. Eng., 2009, 3, (8), pp. 1561–1563.
-
14)
-
4. Logan, B.: ‘Mel frequency cepstral coefficients for music modeling’. Int. Symp. Music Information Retrieval (ISMIR), 2000.
-
15)
-
29. Otsu, N.: ‘A threshold selection method from gray-level histograms’, IEEE Trans. Syst. Man Cybern., 1979, 9, (1), pp. 62–66.
-
16)
-
17. Song, B., Cunwu, H., Dehui, S.: ‘Neural network based method for background modeling and detecting moving objects’, J. China Univ. Posts Telecommun., 2015, 22, (3), pp. 100–109.
-
17)
-
9. Stauffer, C., Grimson, W.E.L.: ‘Adaptive background mixture models for real-time tracking’. 1999 IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, 23–25 June 1999, pp. 246–252.
-
18)
-
14. Hu, W., Tan, T., Wang, L., et al: ‘A survey on visual surveillance of object motion and behaviors’, IEEE Trans. Syst. Man Cybern. C, Appl. Rev., 2004, 34, (3), pp. 334–352.
-
19)
-
5. McIvor, A.: ‘Background subtraction technique’. Proc. of Image & Vision Computing, New Zealand, November 2000, pp. 147–153.
-
20)
-
33. David, A., Sergi, V.: ‘K-means++: the advantage of careful seeding’. SODA ‘07: Proc. of the Eighteenth Annual ACM-SIAM Symp. on Discrete Algorithms, 2007, pp. 1027–1035.
-
21)
-
24. Zotkin, D.N., Duraiswami, R., Davis, L.S.: ‘Joint audio-visual tracking using particle filters’, EURASIP J. Adv. Signal Process., 2002, 2002, (1), pp. 1154–1164.
-
22)
-
26. Chavez-Garcia, R.O., Aycard, O.: ‘Multiple sensor fusion and classification for moving object detection and tracking’, IEEE Trans. Intell. Transp. Syst., 2016, 17, (2), pp. 525–534.
-
23)
-
24)
-
7. Shaikh, S.H., Saeed, K., Chaki, N.: ‘Moving object detection, approaches, challenges and object tracking’ in Shaikh, S.H., Saeed, K., Chaki, N. (Eds.): Moving Object Detection Using Background Subtraction (Springer, London, 2014), pp. 5–14.
-
25)
-
11. Zhou, Z., Jin, Z.: ‘Two-dimension principal component analysis-based motion detection framework with subspace update of background’, IET Comput. Vis., 2016, 10, (6), pp. 603–612.
-
26)
-
18. Qin, H., Zhen, Z., Ma, H.: ‘Moving object detection based on optical flow and neural network fusion’, Int. J. Intell. Comput. Cybern., 2016, 9, (4), pp. 325–335.
-
27)
-
19. Siebel, N.T., Maybank, S.: ‘Fusion of multiple tracking algorithms for robust people tracking’. Computer Vision – ECCV 2002, 2002 (LNCS), (Lecture Notes in Computer Science, Springer, Berlin, 2002), 2353, pp. 373–387.
-
28)
-
10. Crnojević, V., Antić, B., Ćulibrk, D.: ‘Optimal wavelet differencing method for robust motion detection’. 16th IEEE Int. Conf. on Image Processing (ICIP), 2009, pp. 645–648.
-
29)
-
23. Chellappa, R., Qian, G., Zheng, Q.: ‘Vehicle detection and tracking using acoustic and video sensors’. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, 17–21 May 2004, p. iii-793–iii-796.
-
30)
-
16. Han, H., Zhu, J., Liao, S., et al: ‘Moving object detection revisited: speed and robustness’, IEEE Trans. Circuits Syst. Video Technol., 2015, 25, (6), pp. 910–921.
-
31)
-
28. Mehmet, S., Bulent, S.: ‘Survey over image thresholding techniques and quantitative performance evaluation’, J. Electron. Imaging, 2004, 13, (1), pp. 146–168.
-
32)
-
3. Roshan, A., Zhang, Y.: ‘A comparison of moving object detection methods for real-time moving object detection’. SPIE Defense+ Security, Baltimore, USA, 5 May 2014, pp. 907609–907609-6.
-
33)
-
12. Subudhi, B.N., Ghosh, S., Nanda, P.K., et al: ‘Moving object detection using spatio-temporal multilayer compound Markov Random Field and histogram thresholding based change detection’, Multimedia Tools Appl., 2017, 76, (11), pp. 13511–13543.
-
34)
-
27. Dedeoglu, Y.: ‘Moving object detection, tracking and classification for smart video surveillance’. , 2004.
-
35)
-
15. Sadarangani, N.: ‘An improved Gaussian mixture model algorithm for background subtraction’. , Massachusetts Institute of Technology, 2002.
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