@article {12600,
	title = {Estimation of contour motion and deformation for nonrigid object tracking},
	journal = {Journal of the Optical Society of America AJ. Opt. Soc. Am. A},
	volume = {24},
	year = {2007},
	month = {2007///},
	pages = {2109 - 2121},
	abstract = {We present an algorithm for nonrigid contour tracking in heavily cluttered background scenes. Based on the properties of nonrigid contour movements, a sequential framework for estimating contour motion and deformation is proposed. We solve the nonrigid contour tracking problem by decomposing it into three subproblems: motion estimation, deformation estimation, and shape regulation. First, we employ a particle filter to estimate the global motion parameters of the affine transform between successive frames. Then we generate a probabilistic deformation map to deform the contour. To improve robustness, multiple cues are used for deformation probability estimation. Finally, we use a shape prior model to constrain the deformed contour. This enables us to retrieve the occluded parts of the contours and accurately track them while allowing shape changes specific to the given object types. Our experiments show that the proposed algorithm significantly improves the tracker performance.},
	keywords = {Motion detection, VISION},
	doi = {10.1364/JOSAA.24.002109},
	url = {http://josaa.osa.org/abstract.cfm?URI=josaa-24-8-2109},
	author = {Jie Shao and Porikli,Fatih and Chellapa, Rama}
}
@article {14230,
	title = {Motion segmentation using occlusions},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	volume = {27},
	year = {2005},
	month = {2005/06//},
	pages = {988 - 992},
	abstract = {We examine the key role of occlusions in finding independently moving objects instantaneously in a video obtained by a moving camera with a restricted field of view. In this problem, the image motion is caused by the combined effect of camera motion (egomotion), structure (depth), and the independent motion of scene entities. For a camera with a restricted field of view undergoing a small motion between frames, there exists, in general, a set of 3D camera motions compatible with the observed flow field even if only a small amount of noise is present, leading to ambiguous 3D motion estimates. If separable sets of solutions exist, motion-based clustering can detect one category of moving objects. Even if a single inseparable set of solutions is found, we show that occlusion information can be used to find ordinal depth, which is critical in identifying a new class of moving objects. In order to find ordinal depth, occlusions must not only be known, but they must also be filled (grouped) with optical flow from neighboring regions. We present a novel algorithm for filling occlusions and deducing ordinal depth under general circumstances. Finally, we describe another category of moving objects which is detected using cardinal comparisons between structure from motion and structure estimates from another source (e.g., stereo).},
	keywords = {3D motion estimation, algorithms, Artificial intelligence, CAMERAS, Computer vision, Filling, hidden feature removal, Image Enhancement, Image Interpretation, Computer-Assisted, image motion, Image motion analysis, Image segmentation, Layout, MOTION, Motion detection, Motion estimation, motion segmentation, Movement, Object detection, occlusion, occlusions, optical flow, ordinal depth, Pattern Recognition, Automated, Photography, Reproducibility of results, segmentation, Semiconductor device modeling, Sensitivity and Specificity, video analysis., Video Recording},
	isbn = {0162-8828},
	doi = {10.1109/TPAMI.2005.123},
	author = {Ogale, A. S and Ferm{\"u}ller, Cornelia and Aloimonos, J.}
}
@conference {11908,
	title = {Pedestrian classification from moving platforms using cyclic motion pattern},
	booktitle = {IEEE International Conference on Image Processing, 2005. ICIP 2005},
	volume = {2},
	year = {2005},
	month = {2005/09//},
	pages = {II- 854-7 - II- 854-7},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {This paper describes an efficient pedestrian detection system for videos acquired from moving platforms. Given a detected and tracked object as a sequence of images within a bounding box, we describe the periodic signature of its motion pattern using a twin-pendulum model. Then a principle gait angle is extracted in every frame providing gait phase information. By estimating the periodicity from the phase data using a digital phase locked loop (dPLL), we quantify the cyclic pattern of the object, which helps us to continuously classify it as a pedestrian. Past approaches have used shape detectors applied to a single image or classifiers based on human body pixel oscillations, but ours is the first to integrate a global cyclic motion model and periodicity analysis. Novel contributions of this paper include: i) development of a compact shape representation of cyclic motion as a signature for a pedestrian, ii) estimation of gait period via a feedback loop module, and iii) implementation of a fast online pedestrian classification system which operates on videos acquired from moving platforms.},
	keywords = {compact shape representation, cyclic motion pattern, data mining, Detectors, digital phase locked loop, digital phase locked loops, feedback loop module, gait analysis, gait phase information, human body pixel oscillations, HUMANS, image classification, Image motion analysis, image representation, image sequence, Image sequences, Motion detection, Object detection, pedestrian classification, pedestrian detection system, Phase estimation, Phase locked loops, principle gait angle, SHAPE, tracking, Videos},
	isbn = {0-7803-9134-9},
	doi = {10.1109/ICIP.2005.1530190},
	author = {Yang Ran and Qinfen Zheng and Weiss, I. and Davis, Larry S. and Abd-Almageed, Wael and Liang Zhao}
}
@conference {11986,
	title = {Independent motion: the importance of history},
	booktitle = {Computer Vision and Pattern Recognition, 1999. IEEE Computer Society Conference on.},
	volume = {2},
	year = {1999},
	month = {1999///},
	pages = {97 Vol. 2 - 97 Vol. 2},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {We consider a problem central in aerial visual surveillance applications-detection and tracking of small, independently moving objects in long and noisy video sequences. We directly use spatiotemporal image intensity gradient measurements to compute an exact model of background motion. This allows the creation of accurate mosaics over many frames and the definition of a constraint violation function which acts as an indication of independent motion. A novel temporal integration method maintains confidence measures over long subsequences without computing the optic flow, requiring object models, or using a Kalman filler. The mosaic acts as a stable feature frame, allowing precise localization of the independently moving objects. We present a statistical analysis of the effects of image noise on the constraint violation measure and find a good match between the predicted probability distribution function and the measured sample frequencies in a test sequence},
	keywords = {aerial visual surveillance, background image, Fluid flow measurement, Frequency measurement, History, Motion detection, Motion estimation, Motion measurement, Noise measurement, Optical computing, Optical noise, spatiotemporal image intensity gradient measurements, Spatiotemporal phenomena, Surveillance, Video sequences},
	isbn = {0-7695-0149-4},
	doi = {10.1109/CVPR.1999.784614},
	author = {Pless, R. and Brodsky, T. and Aloimonos, J.}
}
