A surveillance video analysis and storage scheme for scalable synopsis browsing

We propose a framework for efficient storing and scalable browsing of surveillance video based on video synopsis. Our framework employs a novel synopsis analysis scheme named Detail-based video synopsis to generate a set of object flags to store and browse surveillance video synopsis. The main contributions of our work are: 1) highlighting important contents of surveillance video; 2) improving the storage efficiency of original video and synopsis video; 3) realizing multi-scale scalable browsing of synopsis video while reserving essential information. The experiments of implementing the framework are shown compared with the previous independent storage method of original video and synopsis video.

[1]  A. Yildiz,et al.  Fast non-linear video synopsis , 2008, 2008 23rd International Symposium on Computer and Information Sciences.

[2]  Yannis Avrithis,et al.  Efficient content representation in MPEG video databases , 1998, Proceedings. IEEE Workshop on Content-Based Access of Image and Video Libraries (Cat. No.98EX173).

[3]  Chong-Wah Ngo,et al.  Automatic video summarization by graph modeling , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[4]  Itu-T and Iso Iec Jtc.  Advanced video coding for generic audiovisual services , 2010 .

[5]  Michael A. Smith,et al.  Video skimming and characterization through the combination of image and language understanding techniques , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[6]  Jon Louis Bentley,et al.  Quad trees a data structure for retrieval on composite keys , 1974, Acta Informatica.

[7]  Yael Pritch,et al.  Clustered Synopsis of Surveillance Video , 2009, 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance.

[8]  Jenq-Neng Hwang,et al.  An integrated scheme for object-based video abstraction , 2000, ACM Multimedia.

[9]  Thomas Sikora,et al.  Extending H.264/AVC with a background sprite prediction mode , 2008, 2008 15th IEEE International Conference on Image Processing.

[10]  Yael Pritch,et al.  Making a Long Video Short: Dynamic Video Synopsis , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[11]  Yael Pritch,et al.  Webcam Synopsis: Peeking Around the World , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[12]  Ba Tu Truong,et al.  Video abstraction: A systematic review and classification , 2007, TOMCCAP.

[13]  Xuelong Li,et al.  Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Quan Pan,et al.  Real-time multiple objects tracking with occlusion handling in dynamic scenes , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[15]  Yueting Zhuang,et al.  Adaptive key frame extraction using unsupervised clustering , 1998, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269).

[16]  Tianzhu Zhang,et al.  Learning semantic scene models by object classification and trajectory clustering , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[17]  Yael Pritch,et al.  This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2008 1 Non-Chronological Video , 2022 .

[18]  Michael R. Frater,et al.  Object-Based Multiple Sprite Coding of Unsegmented Videos using H.264/AVC , 2007, 2007 IEEE International Conference on Image Processing.

[19]  Heiko Schwarz,et al.  MCTF and scalability extension of H.264/AVC and its application to video transmission, storage, and surveillance , 2005, Visual Communications and Image Processing.

[20]  Mubarak Shah,et al.  An object-based video coding framework for video sequences obtained from static cameras , 2005, MULTIMEDIA '05.

[21]  Dacheng Tao,et al.  Biased Discriminant Euclidean Embedding for Content-Based Image Retrieval , 2010, IEEE Transactions on Image Processing.