Abstract
The aim of event detection is to identify interested events in a user-generated content using multiple modalities automatically. However, it is a challenging task particularly when videos are captured in a restricted environment by nonprofessionals. Such videos suffer from poor quality, deprived lighting, blurring, complex camera motion chaotic background clutter, and obstructions. However, with the rise of social media, there is rising popularity of user-generated videos on the Web day-by-day. Each minute, 300 hours of user-generated video are uploaded on you tube due to which people find difficult to search the appropriate content among a large number of videos. Therefore, solutions to this problem are in great demands. In this paper, we study existing technologies for event detection in user-generated videos using multiple modalities. This paper provides key points about feature representations across different modalities, classification techniques.
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Susmitha, A., Jain, S., Mohnaty, M.N. (2021). Automatic Event Detection in User-Generated Video Content: A Survey. In: Mallick, P.K., Bhoi, A.K., Chae, GS., Kalita, K. (eds) Advances in Electronics, Communication and Computing. ETAEERE 2020. Lecture Notes in Electrical Engineering, vol 709. Springer, Singapore. https://doi.org/10.1007/978-981-15-8752-8_29
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DOI: https://doi.org/10.1007/978-981-15-8752-8_29
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