Trajectory-Based Video Retrieval for Multimedia Information Systems

Shim, Choon-Bo; Chang, Jae-Woo; Kim, Young-Chang

doi:10.1007/978-3-540-30198-1_38

Cited by 13 publications

(15 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Automatically estimating motions from an image sequence is a classical computer vision problem that is still actively investigated [5,17,31,33,35,39,40]. Thus, instead of proposing a new method, we aim to use well-established vision techniques to demonstrate the feasibility of built-in support for the direct manipulation of video.…”

Section: Computer Vision Approachesmentioning

confidence: 99%

See 1 more Smart Citation

Video browsing by direct manipulation

Dragicevic

Ramos

Bibliowitcz

et al. 2008

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

133

View full text Add to dashboard Cite

We present a method for browsing videos by directly dragging their content. This method brings the benefits of direct manipulation to an activity typically mediated by widgets. We support this new type of interactivity by: 1) automatically extracting motion data from videos; and 2) a new technique called relative flow dragging that lets users control video playback by moving objects of interest along their visual trajectory. We show that this method can outperform the traditional seeker bar in video browsing tasks that focus on visual content rather than time.

show abstract

Section: Computer Vision Approachesmentioning

confidence: 99%

“…Several research efforts have explored the use of images and visual trajectories for indexing and searching video [31,35]. While some of these tools provide sketch-based GUIs, they all use a conversational interaction paradigm: the user makes a query, and then waits for the results.…”

Section: Content-based Video Retrievalmentioning

confidence: 99%

Video browsing by direct manipulation

Dragicevic

Ramos

Bibliowitcz

et al. 2008

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

133

View full text Add to dashboard Cite

show abstract

“…Previous work has sought to represent moving object trajectories through a wide variety of direction schemes, polynomial models and other function approximations [1][2][3][4][5][6][7][8][9][10][19][20][21][22]. The importance of selecting the most appropriate trajectory model has received relatively scant attention [11].…”

Section: Background and Related Workmentioning

confidence: 99%

“…Much of the earlier research focus has been on high-level object trajectory representation schemes that are able to produce compressed forms of motion data [1][2][3][4][5][6][7][8][9][10][11]. This work presupposes the existence of some low-level tracking scheme for reliably extracting object-based trajectories [12,13].…”

Section: Introductionmentioning

confidence: 99%

Classifying spatiotemporal object trajectories using unsupervised learning of basis function coefficients

Khalid

Naftel

2005

Proceedings of the Third ACM International Workshop on Video Surveillance &Amp; Sensor Networks

View full text Add to dashboard Cite

This paper proposes a novel technique for clustering and classification of object trajectory-based video motion clips using spatiotemporal functional approximations. A Mahalanobis classifier is then used for the detection of anomalous trajectories. Motion trajectories are considered as time series and modeled using the leading Fourier coefficients obtained by a Discrete Fourier Transform. Trajectory clustering is then carried out in the Fourier coefficient feature space to discover patterns of similar object motions. The coefficients of the basis functions are used as input feature vectors to a Self-Organising Map which can learn similarities between object trajectories in an unsupervised manner. Encoding trajectories in this way leads to efficiency gains over existing approaches that use discrete point-based flow vectors to represent the whole trajectory. Experiments are performed on two different datasets -synthetic and pedestrian object tracking -to demonstrate the effectiveness of our approach. Applications to motion data mining in video surveillance databases are envisaged.

show abstract

“…Much of the earlier research focus in motion analysis has been on high-level object trajectory representation schemes that are able to produce compressed forms of motion data (Aghbari et al, 2003;Chang et al, 1998;Dagtas et al, 2000;Hsu & Teng, 2002;Jin & Mokhtarian, 2004;Khalid & Naftel, 2005;Shim & Chang, 2004). This work presupposes the existence of some low-level visual tracking scheme for reliably extracting object-based trajectories (Hu, Tan, Wang & Maybank, 2004;Vlachos et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

Behavior Recognition Using any Feature Space Representation of Motion Trajectories

Khalid¹

2012

Recent Developments in Video Surveillance

View full text Add to dashboard Cite

Trajectory-Based Video Retrieval for Multimedia Information Systems

Cited by 13 publications

References 3 publications

Video browsing by direct manipulation

Video browsing by direct manipulation

Classifying spatiotemporal object trajectories using unsupervised learning of basis function coefficients

Behavior Recognition Using any Feature Space Representation of Motion Trajectories

Contact Info

Product

Resources

About