19.4.5.6.1 Surveillance Video Summarization, Surveillance Synopsis

Chapter Contents (Back)
Video Abstracts. Video Summarization. Surveillance. Summarization.

Kim, C.[Changick], Hwang, J.N.[Jenq-Neng],
Object-based video abstraction for video surveillance systems,
CirSysVideo(12), No. 12, December 2002, pp. 1128-1138.
IEEE Top Reference. 0301
BibRef
Earlier:
Object-based Video Abstraction Using Cluster Analysis,
ICIP01(II: 657-660).
IEEE DOI 0108
BibRef

Pritch, Y.[Yael], Rav-Acha, A.[Alex], Peleg, S.[Shmuel],
Nonchronological Video Synopsis and Indexing,
PAMI(30), No. 11, November 2008, pp. 1971-1984.
IEEE DOI 0809
BibRef
Earlier: A2, A1, A3:
Making a Long Video Short: Dynamic Video Synopsis,
CVPR06(I: 435-441).
IEEE DOI 0606
Partly mosaicing, partly change detection. Capture video and generate a synopsis image that contains the constant background plus some subset of the moving objects. E.g. Street scene plus the pedestrians, or the same scene plus moving cars. BibRef

Pritch, Y.[Yael], Kav-Venaki, E.[Eitam], Peleg, S.[Shmuel],
Shift-map image editing,
ICCV09(151-158).
IEEE DOI 0909
BibRef

Pritch, Y.[Yael], Ratovitch, S.[Sarit], Hendel, A.[Avishai], Peleg, S.[Shmuel],
Clustered Synopsis of Surveillance Video,
AVSBS09(195-200).
IEEE DOI 0909
BibRef

Peleg, S.[Shmuel],
Keynote lecture 2: Video synopsis,
AVSS13(XVII-XVII)
IEEE DOI 1311
object recognition BibRef

Xiang, T.[Tao], Gong, S.G.[Shao-Gang],
Optimising dynamic graphical models for video content analysis,
CVIU(112), No. 3, December 2008, pp. 310-323.
Elsevier DOI 0811
Video content analysis; Structure scoring; Graphical models; Hidden Markov models; Surveillance video segmentation; Group activity modelling See also Beyond Tracking: Modelling Activity and Understanding Behaviour. BibRef

Alexiou, I., Xiang, T.[Tao], Gong, S.G.[Shao-Gang],
Exploring synonyms as context in zero-shot action recognition,
ICIP16(4190-4194)
IEEE DOI 1610
BibRef
Earlier:
Learning a joint discriminative-generative model for action recognition,
WSSIP15(1-4)
IEEE DOI 1603
Computational modeling. gradient methods BibRef

Mehmood, K., Mrak, M.[Marta], Calic, J.[Janko], Kondoz, A.M.[Ahmet M.],
Object tracking in surveillance videos using compressed domain features from scalable bit-streams,
SP:IC(24), No. 10, November 2009, pp. 814-824.
Elsevier DOI 0911
Object tracking; Scalable video coding; Compressed domain analysis; Motion vectors BibRef

Höferlin, M.[Markus], Höferlin, B.[Benjamin], Heidemann, G.[Gunther], Weiskopf, D.[Daniel],
Interactive Schematic Summaries for Faceted Exploration of Surveillance Video,
MultMed(15), No. 4, 2013, pp. 908-920.
IEEE DOI 1307
BibRef
Earlier: A1, A2, A4, A3:
Interactive schematic summaries for exploration of surveillance video,
ICMR11(9).
DOI Link 1301
image motion analysis; video surveillance BibRef

Wang, S.Z.[Shi-Zheng], Wang, Z.Y.[Zhong-Yuan], Hu, R.M.[Rui-Min],
Surveillance video synopsis in the compressed domain for fast video browsing,
JVCIR(24), No. 8, 2013, pp. 1431-1442.
Elsevier DOI 1312
Surveillance video BibRef

Chen, Y.[Ying], Zhang, B.L.[Bai-Ling],
Surveillance video summarisation by jointly applying moving object detection and tracking,
IJCVR(4), No. 3, 2014, pp. 212-234.
DOI Link 1407
BibRef

Huang, C.R.[Chun-Rong], Chung, P.C.J., Yang, D.K.[Di-Kai], Chen, H.C.[Hsing-Cheng], Huang, G.J.[Guan-Jie],
Maximum a Posteriori Probability Estimation for Online Surveillance Video Synopsis,
CirSysVideo(24), No. 8, August 2014, pp. 1417-1429.
IEEE DOI 1410
maximum likelihood estimation BibRef

Cooharojananone, N.[Nagul], Kasamwattanarote, S.[Siriwat], Lipikorn, R.[Rajalida], Satoh, S.[Shin'ichi],
Automated real-time video surveillance summarization framework,
RealTimeIP(10), No. 3, September 2015, pp. 513-532.
Springer DOI 1509
BibRef

Li, X., Wang, Z., Lu, X.,
Surveillance Video Synopsis via Scaling Down Objects,
IP(25), No. 2, February 2016, pp. 740-755.
IEEE DOI 1601
Image coding BibRef

Wang, S.Z.[Shi-Zheng], Yang, J.W.[Jian-Wei], Zhao, Y.Y.[Yan-Yun], Cai, A.[Anni], Li, S.Z.[Stan Z.],
A surveillance video analysis and storage scheme for scalable synopsis browsing,
VS11(1947-1954).
IEEE DOI 1201
BibRef

Cote, M., Jean, F., Albu, A.B., Capson, D.,
Video summarization for remote invigilation of online exams,
WACV16(1-9)
IEEE DOI 1606
Computational modeling BibRef

Zhang, S., Zhu, Y., Roy-Chowdhury, A.K.,
Context-Aware Surveillance Video Summarization,
IP(25), No. 11, November 2016, pp. 5469-5478.
IEEE DOI 1610
Automobiles BibRef

Salehin, M.M.[M. Musfequs], Paul, M.[Manoranjan],
Adaptive fusion of human visual sensitive features for surveillance video summarization,
JOSA-A(34), No. 5, May 2017, pp. 814-826.
DOI Link 1705
Fibers, polarization-maintaining, Lasers, distributed-feedback, Fiber, Bragg gratings BibRef

Panda, R., Roy-Chowdhury, A.K.,
Multi-View Surveillance Video Summarization via Joint Embedding and Sparse Optimization,
MultMed(19), No. 9, September 2017, pp. 2010-2021.
IEEE DOI 1708
Cameras, Correlation, Feature extraction, Linear programming, Optimization, Sparse matrices, Surveillance, Camera network, multi-view video, sparse optimization, video, summarization BibRef


Wang, W.C., Chung, P.C., Huang, C.R., Huang, W.Y.,
Event based surveillance video synopsis using trajectory kinematics descriptors,
MVA17(250-253)
DOI Link 1708
Cameras, Kinematics, Organizations, Streaming media, Surveillance, Trajectory, Visualization BibRef

Panda, R., Dasy, A., Roy-Chowdhury, A.K.,
Video summarization in a multi-view camera network,
ICPR16(2971-2976)
IEEE DOI 1705
Cameras, Correlation, Dictionaries, Linear programming, Optimization, Sparse matrices, Symmetric, matrices BibRef

Annappa, M.[Manish], Chakravarthy, S.[Sharma], Athitsos, V.[Vassilis],
Pre-processing of Video Streams for Extracting Queryable Representation of Its Contents,
ISVC16(II: 301-311).
Springer DOI 1701
inferring situations of interest. BibRef

Lai, P.K.[Po Kong], Décombas, M., Moutet, K., Laganière, R.,
Video summarization of surveillance cameras,
AVSS16(286-294)
IEEE DOI 1611
Acceleration BibRef

Salehin, M.M., Paul, M.,
Summarizing Surveillance Video by Saliency Transition and Moving Object Information,
DICTA15(1-8)
IEEE DOI 1603
image motion analysis BibRef

Hoshen, Y.[Yedid], Peleg, S.[Shmuel],
Live video synopsis for multiple cameras,
ICIP15(212-216)
IEEE DOI 1512
Multi Camera Synopsis; Video Surveillance; Video Synopsis BibRef

Yun, S.[Sangdoo], Yun, K.[Kimin], Kim, S.W.[Soo Wan], Yoo, Y.J.[Young-Joon], Jeong, J.[Jiyeoup],
Visual surveillance briefing system: Event-based video retrieval and summarization,
AVSS14(204-209)
IEEE DOI 1411
Animation BibRef

Choudhary, V.[Vikas], Tiwari, A.K.[Anil K.],
Surveillance Video Synopsis,
ICCVGIP08(207-212).
IEEE DOI 0812
BibRef

Li, J.[Jian], Nikolov, S.G.[Stavri G.], Benton, C.P.[Christopher P.], Scott-Samuel, N.E.[Nicholas E.],
Adaptive summarisation of surveillance video sequences,
AVSBS07(546-551).
IEEE DOI 0709
BibRef

Graves, A., Gong, S.,
Wavelet-based holistic sequence descriptor for generating video summaries,
BMVC04(xx-yy).
HTML Version. 0508
BibRef
Earlier:
Spotting Scene Change for Indexing Surveillance Video,
BMVC03(xx-yy).
HTML Version. 0409
BibRef

Chapter on Implementations and Applications, Databases, QBIC, Video Analysis, Hardware and Software, Inspection continues in
Personal Videos, Consumer Videos, Abstracts, Synopsis, Summarization .


Last update:Nov 18, 2017 at 20:56:18