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SVM-Based classification of moving objects

Li, Z, Jiang, J and Xiao, G (2009) SVM-Based classification of moving objects In: First International Conference, MulGraB 2009, Held as Part of the Future Generation Information Technology Conference, FGIT 2009, 2009-12-10 - 2009-12-12, Jeju Island, Korea.

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In this paper, we propose a single SVM-based algorithm to classify moving objects inside videos and hence extract semantics features for further multimedia processing and content analysis. While standard SVM is a binary classifier and complicated procedures are often required to turn it into a multi-classifier, we introduce a new technique to map the output of a standard SVM directly into posterior probabilities of the moving objects via Sigmoid function. We further add a post-filtering framework to improve its performances of moving object classification by using a weighted mean filter to smooth the classification results. Extensive experiments are carried out and their results demonstrate that the proposed SVM-based algorithm can effectively classify a range of moving objects.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Divisions : Surrey research (other units)
Authors :
Li, Z
Xiao, G
Date : 2009
DOI : 10.1007/978-3-642-10512-8_5
Contributors :
Depositing User : Symplectic Elements
Date Deposited : 17 May 2017 12:25
Last Modified : 23 Jan 2020 17:50

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