Abstract
This paper proposes an intelligent video surveillance system to estimate the crowd density by effective region feature extracting (ERFE) and learning. Firstly, motion detection method is utilized to segment the foreground, and the extremal regions of the foreground are then extracted. Furthermore, a new perspective projection method is proposed to modify the 3D to 2D distortion of the extracted regions, and the moving cast shadow is eliminated based on the color invariant of the shadow region. Afterwards, histogram statistic method is applied to extract crowd features from the modified regions. Finally, the crowd features are classified into a range of density levels by using support vector machine. Experiments on real crowd videos show that the proposed density estimation system has great advantage in large-scale crowd analysis. And more importantly, better performance is achieved even on variant view angle or illumination changing conditions. Thus the video surveillance system is more robust and practical.
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Su, H., Yang, H., Zheng, S. (2011). The Large-Scale Crowd Density Estimation Based on Effective Region Feature Extraction Method. 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_24
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DOI: https://doi.org/10.1007/978-3-642-19318-7_24
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