@conference {12574,
	title = {Robust Visual Tracking Using the Time-Reversibility Constraint},
	booktitle = {Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on},
	year = {2007},
	month = {2007/10//},
	pages = {1 - 8},
	abstract = {Visual tracking is a very important front-end to many vision applications. We present a new framework for robust visual tracking in this paper. Instead of just looking forward in the time domain, we incorporate both forward and backward processing of video frames using a novel time-reversibility constraint. This leads to a new minimization criterion that combines the forward and backward similarity functions and the distances of the state vectors between the forward and backward states of the tracker. The new framework reduces the possibility of the tracker getting stuck in local minima and significantly improves the tracking robustness and accuracy. Our approach is general enough to be incorporated into most of the current tracking algorithms. We illustrate the improvements due to the proposed approach for the popular KLT tracker and a search based tracker. The experimental results show that the improved KLT tracker significantly outperforms the original KLT tracker. The time-reversibility constraint used for tracking can be incorporated to improve the performance of optical flow, mean shift tracking and other algorithms.},
	keywords = {backward, constraint;video, criterion;state, forward, frame, KLT, processing;, processing;video, processing;visual, signal, tracker;minimization, tracking;minimisation;video, vectors;time-reversibility},
	doi = {10.1109/ICCV.2007.4408956},
	author = {Wu,Hao and Chellapa, Rama and Sankaranarayanan,A. C and Zhou,S. K}
}
@conference {12702,
	title = {Robust two-camera tracking using homography},
	booktitle = {Acoustics, Speech, and Signal Processing, 2004. Proceedings. (ICASSP {\textquoteright}04). IEEE International Conference on},
	volume = {3},
	year = {2004},
	month = {2004/05//},
	pages = {iii - 1-4 vol.3 - iii - 1-4 vol.3},
	abstract = {The paper introduces a two view tracking method which uses the homography relation between the two views to handle occlusions. An adaptive appearance-based model is incorporated in a particle filter to realize robust visual tracking. Occlusion is detected using robust statistics. When there is occlusion in one view, the homography from this view to other views is estimated from previous tracking results and used to infer the correct transformation for the occluded view. Experimental results show the robustness of the two view tracker.},
	keywords = {Carlo, filter;, filters;, frame, framework;, homography;, image, method;, methods;, Monte, nonlinear, occlusions;, optical, particle, processing;, robust, sequences;, sequential, signal, statistics;, tracking, tracking;, two, two-camera, video, view, visual},
	doi = {10.1109/ICASSP.2004.1326466},
	author = {Yue,Zhanfeng and Zhou,S. K and Chellapa, Rama}
}
@conference {12721,
	title = {An appearance based approach for human and object tracking},
	booktitle = {Image Processing, 2003. ICIP 2003. Proceedings. 2003 International Conference on},
	volume = {2},
	year = {2003},
	month = {2003/09//},
	pages = {II - 85-8 vol.3 - II - 85-8 vol.3},
	abstract = {A system for tracking humans and detecting human-object interactions in indoor environments is described. A combination of correlogram and histogram information is used to model object and human color distributions. Humans and objects are detected using a background subtraction algorithm. The models are built on the fly and used to track them on a frame by frame basis. The system is able to detect when people merge into groups and segment them during occlusion. Identities are preserved during the sequence, even if a person enters and leaves the scene. The system is also able to detect when a person deposits or removes an object from the scene. In the first case the models are used to track the object retroactively in time. In the second case the objects are tracked for the rest of the sequence. Experimental results using indoor video sequences are presented.},
	keywords = {algorithm;, analysis;, background, basis;, by, Color, colour, correlogram, detection;, distributions;, frame, histogram, human, image, information;, object, processing;, segmentation;, sequences;, signal, subtraction, tracking;, video},
	doi = {10.1109/ICIP.2003.1246622},
	author = {Capellades,M. B and David Doermann and DeMenthon,D. and Chellapa, Rama}
}
@article {18194,
	title = {Data hiding in image and video .II. Designs and applications},
	journal = {Image Processing, IEEE Transactions on},
	volume = {12},
	year = {2003},
	month = {2003/06//},
	pages = {696 - 705},
	abstract = {For pt. I see ibid., vol.12, no.6, p.685-95 (2003). This paper applies the solutions to the fundamental issues addressed in Part I to specific design problems of embedding data in image and video. We apply multilevel embedding to allow the amount of embedded information that can be reliably extracted to be adaptive with respect to the actual noise conditions. When extending the multilevel embedding to video, we propose strategies for handling uneven embedding capacity from region to region within a frame as well as from frame to frame. We also embed control information to facilitate the accurate extraction of the user data payload and to combat such distortions as frame jitter. The proposed algorithm can be used for a variety of applications such as copy control, access control, robust annotation, and content-based authentication.},
	keywords = {access, annotation;, authentication;, capacity;, conditions;, content-based, CONTROL, control;, copy, data, distortions;, EMBEDDING, embedding;, encapsulation;, extraction;, frame, hiding;, image, information;, jitter;, message, multilevel, NOISE, noise;, payload, processing;, robust, signal, uneven, user, video},
	isbn = {1057-7149},
	doi = {10.1109/TIP.2003.810589},
	author = {M. Wu and Yu,H. and Liu,Bede}
}
