Motion detection in complex and dynamic backgrounds

Authors:
Daeyong Park;Junbeom Kim;Jaemin Kim;Seongwon Cho;Sun-Tae Chung
Affiliations:
School of Electronics and Electrical Engineering, Hongik University, Seoul, Korea;School of Electronics and Electrical Engineering, Hongik University, Seoul, Korea;School of Electronics and Electrical Engineering, Hongik University, Seoul, Korea;School of Electronics and Electrical Engineering, Hongik University, Seoul, Korea;School of Electronics Engineering, Soongsil University
Venue:
PSIVT'06 Proceedings of the First Pacific Rim conference on Advances in Image and Video Technology
Year:
2006

Citing 2
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Abstract

For the detection of moving objects, background subtraction methods are widely used. In case the background changes, we need to update the background in real-time for the reliable detection of foreground objects. An adaptive Gaussian mixture model (GMM) combined with probabilistic learning is one of the most popular methods for the real-time update of the complex and dynamic background. However, the probabilistic learning approach does not work well in high traffic regions. In this paper, we classify each pixel into four different types: still background, dynamic background, moving object, and still object, and update the background model based on the classification. For the classification, we analyze a sequence of frame differences at each pixel and its neighborhood. We experimentally show that the proposed method learn complex and dynamic backgrounds in high traffic regions more reliably, compared with traditional methods.