Proficient Approaches for Human Action Recognition and Prediction in Surveillance Videos
| dc.contributor.guide | Vadivu, G | |
| dc.coverage.spatial | ||
| dc.creator.researcher | Poonkodi, M | |
| dc.date.accessioned | 2022-07-04T06:32:13Z | |
| dc.date.available | 2022-07-04T06:32:13Z | |
| dc.date.awarded | ||
| dc.date.completed | 2022 | |
| dc.date.registered | ||
| dc.description.abstract | Computer Vision is playing a remarkable role right from essentials to newlineentertainment and thus trying to turn computer as a seeing machine. Though Computer newlinevision has widespread applications in most of the real world domains like healthcare, newlinesurveillance, space and social media, it is still not able to efficiently and effectively address newlinethe challenge of matching human intelligence in understanding the underlying context. newlineIntelligent video classification and prediction is a fundamental step towards effective newlineretrieval system. A huge volume of video is available for navigation. Managing such videos newlineand prediction of the activity before its completion gains importance in video surveillance, newlinehuman computer recognition, gesture recognition etc newline | |
| dc.description.note | ||
| dc.format.accompanyingmaterial | DVD | |
| dc.format.dimensions | ||
| dc.format.extent | ||
| dc.identifier.uri | http://hdl.handle.net/10603/390658 | |
| dc.language | English | |
| dc.publisher.institution | Department of Computer Science Engineering | |
| dc.publisher.place | Kattankulathur | |
| dc.publisher.university | SRM Institute of Science and Technology | |
| dc.relation | ||
| dc.rights | university | |
| dc.source.university | University | |
| dc.subject.keyword | Computer Science | |
| dc.subject.keyword | Computer Science Hardware and Architecture | |
| dc.subject.keyword | Engineering and Technology | |
| dc.title | Proficient Approaches for Human Action Recognition and Prediction in Surveillance Videos | |
| dc.title.alternative | ||
| dc.type.degree | Ph.D. |
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