Original scientific paper
https://doi.org/10.32985/ijeces.14.9.3
An Enhanced Spatio-Temporal Human Detected Keyframe Extraction
D Rajeshwari
; Research Department of Computer Science Shrimathi Devkunvar Nanalal Bhatt Vaishnav College for Women, Affiliated to University of Madras, Chennai, India.
*
C Victoria Priscilla
; PG Department of Computer Science, Shrimathi Devkunvar Nanalal Bhatt Vaishnav College for Women, Affiliated to University of Madras, Chennai, India.
* Corresponding author.
Abstract
Due to the immense availability of Closed-Circuit Television surveillance, it is quite difficult for crime investigation due to its huge storage and complex background. Content-based video retrieval is an excellent method to identify the best Keyframes from these surveillance videos. As the crime surveillance reports numerous action scenes, the existing keyframe extraction is not exemplary. At this point, the Spatio-temporal Histogram of Oriented Gradients - Support Vector Machine feature method with the combination of Background Subtraction is appended over the recovered crime video to highlight the human presence in surveillance frames. Additionally, the Visual Geometry Group trains these frames for the classification report of human-detected frames. These detected frames are processed to extract the keyframe by manipulating an inter-frame difference with its threshold value to favor the requisite human-detected keyframes. Thus, the experimental results of HOG-SVM illustrate a compression ratio of 98.54%, which is preferable to the proposed work's compression ratio of 98.71%, which supports the criminal investigation.
Keywords
Histogram of Oriented Gradients-Support Vector Machine; Keyframe Extraction; Spatio-temporal feature Extraction; Content- Based Video Retrieval;
Hrčak ID:
309720
URI
Publication date:
14.11.2023.
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