Abstract
Huge amount of user-generated video content is shared and downloaded by millions of users across the globe, every minute. The sudden raise of this web content made event detection in videos a major demanding field of research in recent days. Detecting events automatically in large user-generated video datasets (UGV) is a challenging task because of its poor quality and un structured content. In this work, random forest classifier is used to detect events in UCF 101 dataset by utilizing handcrafted features like HOG and Tamura. The performance is measured in terms of accuracy, specificity, sensitivity, false discovery rate, false omission rate, and error rate.
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Susmitha, A., Jain, S., Mohanty, M.N. (2021). Random Forest Classification-Based Video Event Detection Utilizing Hand Crafted Features. In: Das, S., Mohanty, M.N. (eds) Advances in Intelligent Computing and Communication. Lecture Notes in Networks and Systems, vol 202. Springer, Singapore. https://doi.org/10.1007/978-981-16-0695-3_60
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DOI: https://doi.org/10.1007/978-981-16-0695-3_60
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