Abstract
Video metadata is an underlying basis for intelligent traffic applications such as detection, recognition and segmentation in traffic surveillance scene. However, it is still a challenge to organise efficiently the large-scale data and retrieve some important traffic surveillance metadata. In this paper, we introduce a novel metadata organization and retrieval approach for massive surveillance video. Firstly, we propose a tree-like structure called attribute tree, which is based on the characteristics of balance tree to organise and index video metadata. In the metedata organization phrase, we combine the spatio-temporal attributes (e.g.,camera location, time interval) and the visual semantic attributes (e.g.,vehicle type, vehicle color) to build up a hierarchical metadata organising structure with our attribute tree. Secondly, by taking use of the semantic attribute tree and visual feature vector, we propose an efficient retrieval method to query metadata. Thirdly, combining by Hadoop distributed processing and Hbase database, we implement a video metadata organization and retrieval system. The experimental results with the real traffic surveillance metadata demonstrate the retrieval efficiency with our approach.
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© 2015 Springer International Publishing Switzerland
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Tang, Y., Zhang, H., Xu, B. (2015). Metadata Organization and Retrieval with Attribute Tree for Large-Scale Traffic Surveillance Videos. In: Wang, Y., Xiong, H., Argamon, S., Li, X., Li, J. (eds) Big Data Computing and Communications. BigCom 2015. Lecture Notes in Computer Science(), vol 9196. Springer, Cham. https://doi.org/10.1007/978-3-319-22047-5_35
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DOI: https://doi.org/10.1007/978-3-319-22047-5_35
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