Key Feature Extraction for Video Shot Boundary Detection using CNN
Neelam Labhade Kumar1, Yogeshkumar Sharma2, Parul S. Arora3
1Neelam Labhade-Kumar, Research Scholar, J. J. T. University, Rajasthan. Assistance Prof. JSPM’s ICOER Wagholi, Maharashtra, India.
2Dr. Yogeshkumar Sharma, Associate Prof. Shri J. J. T. University, Churella, Jhunjhunu, India.
3Dr. Parul S. Arora, Associate Prof. JSPM’s ICOER Wagholi, Maharashtra, India. 

Manuscript received on January 05, 2020. | Revised Manuscript received on January 25, 2020. | Manuscript published on January 30, 2020. | PP: 4763-4769 | Volume-8 Issue-5, January 2020. | Retrieval Number: E6789018520/2020©BEIESP | DOI: 10.35940/ijrte.E6789.018520

Open Access | Ethics and Policies | Cite  | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (

Abstract: Now days as the progress of digital image technology, video files raise fast, there is a great demand for automatic video semantic study in many scenes, such as video semantic understanding, content-based analysis, video retrieval. Shot boundary detection is an elementary step for video analysis. However, recent methods are time consuming and perform badly in the gradual transition detection. In this paper we have projected a novel approach for video shot boundary detection using CNN which is based on feature extraction. We designed couple of steps to implement this method for automatic video shot boundary detection (VSBD). Primarily features are extracted using H, V&S parameters based on mean log difference along with implementation of histogram distribution function. This feature is given as an input to CNN algorithm which detects shots which is based on probability function. CNN is implemented using convolution and rectifier linear unit activation matrix which is followed after filter application and zero padding. After downsizing the matrix it is given as a input to fully connected layer which indicates shot boundaries comparing the proposed method with CNN method based on GPU the results are encouraging with substantially high values of precision Recall & F1 measures. CNN methods perform moderately better for animated videos while it excels for complex video which is observed in the results.
Keywords: Video Shot Boundary Detection (VSBD), Convolutional Neural Networks (CNN). Rectifier linear Unit (ReLU).
Scope of the Article: Sensor Networks, Actuators for Internet of Things.