Video hierarchical and partitioned storage system based on content features
A technology of content characteristics and hierarchical storage, applied in special data processing applications, instruments, electronic digital data processing, etc., can solve problems such as inability to separate or hierarchical storage, high cost of management and archiving, and inability to satisfy visitors, etc., to improve data re-use Utilization, easy and more user-friendly retrieval, and the effect of reducing storage costs
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Embodiment 2
[0029] Embodiment two is obtained on the basis of embodiment one, and the same part will not be repeated, (please refer to figure 2 ) The CA-SDG module also includes a content feature analysis service module 7, which is arranged between the video source end management scheduling unit 4 and the hierarchical storage decision-making module 5, and is responsible for analyzing and distinguishing the content features of the video source data. Image, with pre-defined video behavior, text information, and structured data extracted from video content for analysis, such as video structure analysis, image feature analysis, image background analysis, video summarization technology, face recognition technology, key frame Extraction technology, license plate recognition technology, vehicle type recognition technology, body color recognition technology, video enrichment technology, subtitle analysis technology, multimedia search technology, media content search technology, etc.), and the ana...
Embodiment 3
[0030] Embodiment three is obtained on the basis of embodiment two, and the same part will not be repeated, (please refer to image 3) There is also a content extraction module 8 between the content characteristic analysis service module 7 in the CA-SDG module and the hierarchical storage decision module 5, which is used to filter and analyze the data analyzed by the content characteristic analysis service module 7 again Carry out filter analysis and content extraction with the video content that state mark is S3, and the state mark of the content (extracted as picture, XML text information) of filter analysis and extraction is S3-Ni; According to embodiment two, for content feature analysis The service module 7 analyzes abnormal behavior libraries such as the blacklist library, deployment control library, and deck library to further filter and analyze, such as analyzing the deck library separately, and extracting the XML text information, picture information, and place informa...
Embodiment 4
[0031] Embodiment 4 is based on Embodiment 2 or Embodiment 3, and the same part will not be repeated; (please refer to Figure 4 , Figure 5 ) The CA-SDG module also includes a feature selection strategy server 9, which is located between the video source end management scheduling unit 4 and the content feature analysis service module 7, and is responsible for feature selection of the video sources that are distinguished, and Sent to the content feature analysis service module to carry out content feature analysis; according to embodiment two or embodiment three, the vehicle A in the S1 video data is carried out the feature information detection extraction such as license plate, vehicle body, car logo, vehicle type, after extracting successfully, change and mark as S2. Hierarchical storage decision-making module 5 extracts and analyzes information based on content feature analysis service module 7 (or cluster) or makes a decision based on existing settings, and compares the v...
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