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17 April 2019 What and where you have seen? Bag of Words-based local feature pooling for visual event detection
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The security analysis of sensitive issues and medical diagnosis are immensely focus to determine exact location of event happening regions. In this paper we propose a model of clustering and pooling techniques to local features using Bag-of-Words (BoW) descriptor in SVM framework for event detection in video sequences. The proposed model extracts local features from six categories of Columbia Consumer Video (CCV) event detection benchmark. We developed the clusters of these features using KD-search tree and Lloyds algorithm. The clusters of features is pooled to vectors by using bag of words model. Introducing the inferring temporal instance labelling, the model performed fast for event detection. The significant performance of the research problem can thrilled out the social media by retrieving the best possible content. The proposed model can efficiently perform the experiments of event detection related to big data problem in visual media. Furthermore, the proposed approach in the model is invariant to rotation, translation and scale changes in the video sequence and robust to the illumination and viewpoints.
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N. Kumar and N. Sukavanam "What and where you have seen? Bag of Words-based local feature pooling for visual event detection", Proc. SPIE 11071, Tenth International Conference on Signal Processing Systems, 110710Q (17 April 2019);

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