Research Article
Video Content Analysis Using Deep Learning Methods
@INPROCEEDINGS{10.1007/978-3-031-35081-8_18, author={Gara Kiran Kumar and Athota Kavitha}, title={Video Content Analysis Using Deep Learning Methods}, proceedings={Intelligent Systems and Machine Learning. First EAI International Conference, ICISML 2022, Hyderabad, India, December 16-17, 2022, Proceedings, Part II}, proceedings_a={ICISML PART 2}, year={2023}, month={7}, keywords={video content segmentation video content recognition classification Deep Convolution Neural Network (DCNN) with Deer Hunting Optimization (DHO)}, doi={10.1007/978-3-031-35081-8_18} }- Gara Kiran Kumar
Athota Kavitha
Year: 2023
Video Content Analysis Using Deep Learning Methods
ICISML PART 2
Springer
DOI: 10.1007/978-3-031-35081-8_18
Abstract
With the emergence of low-cost video recording devices, the internet is flooded with videos. However, most videos are uncategorized, necessitating video content analysis. This review effort addresses visual big data feature extraction, video segmentation, classification, and abstract video challenges. Exploring compressive sensing, deep learning (DL), and kernel methods for various tasks in video content analysis include video classification, clustering, dimension reduction, event detection, and activity recognition. DL is used to examine video footage recognition and classification. This study examines the algorithms’ flaws and benefits when applied to datasets. The classification approaches used Naive Bayes, support vector machine (SVM), and Deep Convolution Neural Network (DCNN) with Deer Hunting Optimization (DHO). Other approaches have higher false discovery and alarm rates than the DCNNDHO algorithm.
