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- W3138666989 abstract "AbstractThe popularity of video recordings either on mobile devices or video surveillance has contributed to the demand for video data applications. As a result, the management of video has become significant in the object retrieval process. Video object retrieval is seen as a major issue in the management of video objects. Object tracking is therefore a significant step in detecting and tracking video objects. Hence, object tracking is a major step, which detects and tracks the video objects using a hybrid model named Nearest Search Algorithm–Nonlinear Autoregressive Exogenous Neural Network. The tracked events are further employed for training, and the recognized events interrelated to the query is processed by proposed Naïve Bayes classifier for retrieving the highly relevant object trajectories and the events associated with it. The performance of the proposed Naïve Bayes classifier is greater when compared with other existing techniques with maximal precision of 81.985%, recall of 85.451%, and F-measure of 86.847%, respectively.KeywordsVideo surveillanceNaive bayes classifierVideo retrievalNeural network" @default.
- W3138666989 created "2021-03-29" @default.
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- W3138666989 date "2021-01-01" @default.
- W3138666989 modified "2023-09-26" @default.
- W3138666989 title "Naive Bayes Approach for Retrieval of Video Object Using Trajectories" @default.
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- W3138666989 doi "https://doi.org/10.1007/978-981-33-6176-8_12" @default.
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