Publication

Abstract : This paper addresses the efficient identification of candidate keyframes in long and untrimmed surveillance videos. Existing fixed- length segmentation methods for violence detection suffer from computational inefficiency and fragmented short-term event repre- sentation. To overcome these limitations, we propose an Optimal Keyframe Extraction framework utilizing shot boundary detection to segment videos based on significant visual changes. Candidate Keyframe segments are extracted from the shots, focusing on potential anomalous segments. Keyframes are selected based on the highest motion changes, accurately representing short term dynamic events. This dynamic approach aims to minimize the processing time of long and untrimmed surveillance videos by reducing the number of segments required for analysis of the event. Results show that the complexity of analyzing the event ef- fectively can be done by extracting an optimal set of keyframes within 2-3% of total number frames for each video. This research improves violence detection by efficiently analyzing large video datasets and accurately identifying short-term dynamic events, thereby enhancing public safety and security applications.