A new 3D convolutional neural network (3D-CNN) framework for multimedia event detection

• Published in: Signal, image and video processing Vol. 15; no. 4; pp. 779 - 787
• Main Authors: Kanagaraj, Kaavya, Priya, G. G. Lakshmi
• Format: Journal Article
• Language: English
• Published: London Springer London 01.06.2021; Springer Nature B.V
• Subjects: Artificial neural networks; Classification; Computer Imaging; Computer Science; Feature extraction; Image Processing and Computer Vision; Multimedia; Multimedia Information Systems; Neural networks; Original Paper; Pattern Recognition and Graphics; Signal,Image and Speech Processing; Video data; Vision; Feature extraction; Mean average precision; Columbia consumer video; Multimedia event detection; 3D convolutional neural network; Classification
• ISSN: 1863-1703; 1863-1711
• DOI: 10.1007/s11760-020-01796-z

Multimedia event detection has received a great deal of interest due to developments in video technology and an increase in multimedia data. However, complexities of video content such as noisy, overlapping, repeated interaction between individuals, and various scenes are becoming difficult for characterizing the subjects and concepts. In particular, Internet users find it difficult to search for a specified event. To solve the above problem, a method is proposed that best suits for event detection, demonstrating the 3D convolutional neural network (3D-CNN) structure to accomplish promising performance in multimedia event classification. To take an advantage of motion content of the event in the video, temporal axis is considered. Both the feature extraction and classification are incorporated in this model. Experiments are carried out on the Columbia Consumer Video benchmark dataset, and results are compared with other existing works.