Abstract
Discovering events from video streams improves the access and reuse of large video collections. Since temporal information is critical in conveying video events, in this paper, a temporal-based event detection framework is proposed to support high-level event detection. The core is a temporal association mining process that systematically captures characteristic temporal patterns to help identify and define interesting events. This framework effectively tackles the challenges caused by loose video structure and class imbalance issues.
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Chen, M. (2012). Video Event Detection Based on Temporal Pattern Analysis. In: Qian, Z., Cao, L., Su, W., Wang, T., Yang, H. (eds) Recent Advances in Computer Science and Information Engineering. Lecture Notes in Electrical Engineering, vol 124. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25781-0_19
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DOI: https://doi.org/10.1007/978-3-642-25781-0_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25780-3
Online ISBN: 978-3-642-25781-0
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