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Video abstraction using density-based clustering algorithm
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DOI：10.1007/s00371-017-1432-3.png)
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The exponential growth in the number of surveillance videos makes the search and retrieval of their contents an extensive, time-consuming, and tedious task. Video abstraction is a general solution to alleviate this problem by generating a short and concise version of the original video. The existing abstraction approaches have commonly relied on global characteristics of visual content and neglected the local details of video frames. This paper presents an enhanced video abstraction approach called Density-based Surveillance video abstraction (DbSva) to generate a static short-length video. The novelty of DbSva is (a) to integrate the advantages of both the global and local features of video contents by fusion and (b) to employ the DENsity-based CLUstEring algorithm (DENCLUE) to significantly improve the quality of abstract videos. Utilizing fusion and the DENCLUE algorithm resulted in the extraction of more informative parts of the videos and increased the robustness of the proposed approach to handle large-scale and noisy videos with no further tuning of the input parameters. A number of qualitative and quantitative experiments support the effectiveness of the proposed approach in generating higher-quality abstract videos compared to the other approaches.
Keyword:
Density-based clustering
Video abstraction
Static video summary
Video summarization
Video analysis
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