Abstract
The potential applications of video surveillance to the Business Intelligence domain continue to grow. For example, automatic computer vision algorithms can provide a fast, efficient process to screen hundreds of hours of video for activity patterns that potentially impact the business. Two such algorithms and their variants are discussed in this chapter. These algorithms analyze surveillance video in order to automatically recognize various functional elements, such as: walkways, roadways, parking-spots, and doorways, through their interactions with pedestrian and vehicle detections. The recognized functional element regions provide a means of capturing statistics related to particular businesses. For example, the owner may be interested in the number of people that enter or exit their business versus the number of people that walk past. Results are shown on functional element recognition and business related activity profiles that demonstrate the effectiveness of these algorithms. Experiments are performed using webcam video of a downtown main street in Ocean City NJ, and surveillance video from the CAVIAR shopping center dataset.
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Swears, E., Turek, M., Collins, R., Perera, A.G.A., Hoogs, A. (2012). Automatic Activity Profile Generation from Detected Functional Regions for Video Scene Analysis. In: Shan, C., Porikli, F., Xiang, T., Gong, S. (eds) Video Analytics for Business Intelligence. Studies in Computational Intelligence, vol 409. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28598-1_8
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DOI: https://doi.org/10.1007/978-3-642-28598-1_8
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