Video Content Analysis Using Deep Learning Models
Bodapati Narasimha Rao, Yedidha M S D Sastry, B. Ganga Bhavani, Srigiri Sri Venumadhav, Mutcharla Venkata Krishna Subash, Kallakuri N V P S Brahma Ramesh · 2023
Videos are abundant online due to the proliferation of inexpensive video recording equipment. However, video content analysis is necessary since most videos cannot be easily classified. Abstract videos, segmentation, and classification are only some video processing issues reviewed here. Examining the potential of kernel techniques, machine learning (ML), and compressive sensing for classification, clustering, reduction of dimensionality, event being identified, and activity identification are all examples of tasks included in video content analysis. Recognition and categorization of video material are investigated using ML. Identification and categorization of video material are investigated using ML. In this research, we look at the strengths and weaknesses of the algorithms in practice. Naive techniques for categorization were utilized. In addition to Bayes and SVM, we also have DCNN equipped with DHO (Deer Hunting Optimization). Compared to the DCNNDHO algorithm, other methods tend to have more excellent rates of false discovery and false alarms.