Abstract
Detecting moving objects against dynamic backgrounds remains a challenge in computer vision and robotics. This paper presents a surprisingly simple algorithm to detect objects in such conditions. Based on theoretic analysis, we show that 1) the displacement of the foreground and the background can be represented by the phase change of Fourier spectra, and 2) the motion of background objects can be extracted by Phase Discrepancy in an efficient and robust way. The algorithm does not rely on prior training on particular features or categories of an image and can be implemented in 9 lines of MATLAB code.
In addition to the algorithm, we provide a new database for moving object detection with 20 video clips, 11 subjects and 4785 bounding boxes to be used as a public benchmark for algorithm evaluation.
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References
Stauffer, C., Grimson, W.: Adaptive background mixture models for real-time tracking. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition, vol. 2, pp. 246–252 (1999)
Mittal, A., Paragios, N.: Motion-based background subtraction using adaptive kernel density estimation. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition, vol. 2 (2004)
Cheung, S., Kamath, C.: Robust techniques for background subtraction in urban traffic video. In: Video Communications and Image Processing, SPIE Electronic Imaging, vol. 5308, pp. 881–892 (2004)
Viola, P., Jones, M.: Robust real-time face detection. International Journal of Computer Vision 57, 137–154 (2004)
Dollár, P., Wojek, C., Schiele, B., Perona, P.: Pedestrian detection: A benchmark. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition, pp. 304–311 (2009)
Tian, T., Tomasi, C., Heeger, D.: Comparison of approaches to egomotion computation. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition, pp. 315–320 (1996)
Han, M., Kanade, T.: Reconstruction of a scene with multiple linearly moving objects. International Journal of Computer Vision 59, 285–300 (2004)
Irani, M., Anandan, P.: A unified approach to moving object detection in 2D and 3D scenes. IEEE Transactions on Pattern Analysis and Machine Intelligence 20, 577–589 (1998)
Vernon, D.: Fourier vision: segmentation and velocity measurement using the Fourier transform. Kluwer Academic Publishers, Dordrecht (2001)
Black, M., Anandan, P.: A framework for the robust estimation of optical flow. In: Proc. IEEE Conf. on International Conference of Computer Vision, pp. 231–236 (1993)
Itti, L., Koch, C., Niebur, E., et al.: A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. on Pattern Analysis and Machine Intelligence 20, 1254–1259 (1998)
Mallat, S.: A wavelet tour of signal processing. Academic Press, London (1999)
Bashir, F., Porikli, F.: Performance evaluation of object detection and tracking systems. In: IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS 2006) (2006)
Sonka, M., Hlavac, V., Boyle, R.: Image Processing, Analysis, and Machine Vision. Cengage-Engineering (2007)
Hou, X., Zhang, L.: Dynamic Visual Attention: Searching for coding length increments. In: Advances in Neural Information Processing Systems, vol. 21, pp. 681–688 (2008)
Itti, L., Baldi, P.: Bayesian surprise attracts human attention, pp. 547–554 (2006)
List, T., Bins, J., Vazquez, J., Fisher, R.: Performance evaluating the evaluator. In: Proc. IEEE Joint Workshop on Visual Surveillance and Performance Analysis of Video Surveillance and Tracking (2005)
Hou, X., Zhang, L.: Saliency detection: A spectral residual approach. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2007), pp. 1–8. IEEE Computer Society, Citeseer (2007)
Guo, C., Ma, Q., Zhang, L.: Spatio-temporal saliency detection using phase spectrum of quaternion fourier transform. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2008)
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Zhou, B., Hou, X., Zhang, L. (2011). A Phase Discrepancy Analysis of Object Motion. In: Kimmel, R., Klette, R., Sugimoto, A. (eds) Computer Vision – ACCV 2010. ACCV 2010. Lecture Notes in Computer Science, vol 6494. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19318-7_18
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DOI: https://doi.org/10.1007/978-3-642-19318-7_18
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