Optimization effect of video data extraction and search based on Faster-RCNN hybrid model on intelligent information systems
The swift surge of video content depicts enormous challenges for intelligent information systems in extracting and searching video data. This article explores improvements achieved by introducing a Faster-RCNN hybrid model into video data extraction and search processes. We propose a novel methodolo...
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| Format: | Article |
| Language: | English |
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De Gruyter
2025-08-01
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| Series: | Nonlinear Engineering |
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| Online Access: | https://doi.org/10.1515/nleng-2025-0133 |
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| author | Zhang Pei Chen Zhengyi |
| author_facet | Zhang Pei Chen Zhengyi |
| author_sort | Zhang Pei |
| collection | DOAJ |
| description | The swift surge of video content depicts enormous challenges for intelligent information systems in extracting and searching video data. This article explores improvements achieved by introducing a Faster-RCNN hybrid model into video data extraction and search processes. We propose a novel methodology combining Faster-RCNN with adaptive feature fusion and temporal coherence modeling to enhance object detection and tracking within video streams substantially. Using a large multi-megavideo dataset called VideoDiv-10K, which is made up of 10,000 videos from categories and others made available through public datasets like ImageNet VID and MOT16, this proposed model exhibited magnificent improvements. This model, when hybrid used with 500 h of content diversity, produced an average mean average precision of 0.891 and had an improved amount by 18.7% more than traditional Faster-RCNN. The model reduced its time for computation by 32.4% and had a massive leap in the accuracy in the search by 41.2%. Key to this performance were the temporal coherence module, which does a good job in capturing dependencies within sequential frames, and the adaptive feature fusion mechanism that dynamically integrates spatial and temporal features. Those were used to attain average F1-score in object detection of 0.937 and 0.912 object tracking on some video resolutions and frame rates. The results of the experiment clearly prove the ability of our hybrid model to process large video data, with significant gains in both accuracy and speed. We then provide better performance within applications such as surveillance, autonomous driving, and content-based video retrieval. Our model maintains a high level of scalability and does not degrade for huge datasets. |
| format | Article |
| id | doaj-art-dae10e4d821648b7bf42ee9c8653527a |
| institution | Kabale University |
| issn | 2192-8029 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Nonlinear Engineering |
| spelling | doaj-art-dae10e4d821648b7bf42ee9c8653527a2025-08-25T06:11:00ZengDe GruyterNonlinear Engineering2192-80292025-08-0114111210.1515/nleng-2025-0133Optimization effect of video data extraction and search based on Faster-RCNN hybrid model on intelligent information systemsZhang Pei0Chen Zhengyi1Dance Academy, Sangmyung University, Seoul, 03015, KoreaDance Academy, Sangmyung University, Seoul, 03015, KoreaThe swift surge of video content depicts enormous challenges for intelligent information systems in extracting and searching video data. This article explores improvements achieved by introducing a Faster-RCNN hybrid model into video data extraction and search processes. We propose a novel methodology combining Faster-RCNN with adaptive feature fusion and temporal coherence modeling to enhance object detection and tracking within video streams substantially. Using a large multi-megavideo dataset called VideoDiv-10K, which is made up of 10,000 videos from categories and others made available through public datasets like ImageNet VID and MOT16, this proposed model exhibited magnificent improvements. This model, when hybrid used with 500 h of content diversity, produced an average mean average precision of 0.891 and had an improved amount by 18.7% more than traditional Faster-RCNN. The model reduced its time for computation by 32.4% and had a massive leap in the accuracy in the search by 41.2%. Key to this performance were the temporal coherence module, which does a good job in capturing dependencies within sequential frames, and the adaptive feature fusion mechanism that dynamically integrates spatial and temporal features. Those were used to attain average F1-score in object detection of 0.937 and 0.912 object tracking on some video resolutions and frame rates. The results of the experiment clearly prove the ability of our hybrid model to process large video data, with significant gains in both accuracy and speed. We then provide better performance within applications such as surveillance, autonomous driving, and content-based video retrieval. Our model maintains a high level of scalability and does not degrade for huge datasets.https://doi.org/10.1515/nleng-2025-0133faster-rcnnvideo data extractionobject detectiontemporal coherenceintelligent information systems |
| spellingShingle | Zhang Pei Chen Zhengyi Optimization effect of video data extraction and search based on Faster-RCNN hybrid model on intelligent information systems Nonlinear Engineering faster-rcnn video data extraction object detection temporal coherence intelligent information systems |
| title | Optimization effect of video data extraction and search based on Faster-RCNN hybrid model on intelligent information systems |
| title_full | Optimization effect of video data extraction and search based on Faster-RCNN hybrid model on intelligent information systems |
| title_fullStr | Optimization effect of video data extraction and search based on Faster-RCNN hybrid model on intelligent information systems |
| title_full_unstemmed | Optimization effect of video data extraction and search based on Faster-RCNN hybrid model on intelligent information systems |
| title_short | Optimization effect of video data extraction and search based on Faster-RCNN hybrid model on intelligent information systems |
| title_sort | optimization effect of video data extraction and search based on faster rcnn hybrid model on intelligent information systems |
| topic | faster-rcnn video data extraction object detection temporal coherence intelligent information systems |
| url | https://doi.org/10.1515/nleng-2025-0133 |
| work_keys_str_mv | AT zhangpei optimizationeffectofvideodataextractionandsearchbasedonfasterrcnnhybridmodelonintelligentinformationsystems AT chenzhengyi optimizationeffectofvideodataextractionandsearchbasedonfasterrcnnhybridmodelonintelligentinformationsystems |