@article {12478,
	title = {Video Metrology Using a Single Camera},
	journal = {Pattern Analysis and Machine Intelligence, IEEE Transactions on},
	volume = {32},
	year = {2010},
	month = {2010/07//},
	pages = {1329 - 1335},
	abstract = {This paper presents a video metrology approach using an uncalibrated single camera that is either stationary or in planar motion. Although theoretically simple, measuring the length of even a line segment in a given video is often a difficult problem. Most existing techniques for this task are extensions of single image-based techniques and do not achieve the desired accuracy especially in noisy environments. In contrast, the proposed algorithm moves line segments on the reference plane to share a common endpoint using the vanishing line information followed by fitting multiple concentric circles on the image plane. A fully automated real-time system based on this algorithm has been developed to measure vehicle wheelbases using an uncalibrated stationary camera. The system estimates the vanishing line using invariant lengths on the reference plane from multiple frames rather than the given parallel lines, which may not exist in videos. It is further extended to a camera undergoing a planar motion by automatically selecting frames with similar vanishing lines from the video. Experimental results show that the measurement results are accurate enough to classify moving vehicles based on their size.},
	keywords = {camera;vanishing, circles;uncalibrated, concentric, image-based, information;vehicle, line, measurement;video, metrology;cameras;image, processing;, segment;multiple, segmentation;video, signal, single, techniques;line, wheelbase},
	isbn = {0162-8828},
	doi = {10.1109/TPAMI.2010.26},
	author = {Guo,Feng and Chellapa, Rama}
}
@conference {12697,
	title = {View independent human body pose estimation from a single perspective image},
	booktitle = {Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on},
	volume = {2},
	year = {2004},
	month = {2004/07/02/june},
	pages = {II-16 - II-22 Vol.2 - II-16 - II-22 Vol.2},
	abstract = {Recovering the 3D coordinates of various joints of the human body from an image is a critical first step for several model-based human tracking and optical motion capture systems. Unlike previous approaches that have used a restrictive camera model or assumed a calibrated camera, our work deals with the general case of a perspective uncalibrated camera and is thus well suited for archived video. The input to the system is an image of the human body and correspondences of several body landmarks, while the output is the set of 3D coordinates of the landmarks in a body-centric coordinate system. Using ideas from 3D model based invariants, we set up a polynomial system of equations in the unknown head pitch, yaw and roll angles. If we are able to make the often-valid assumption that the torso twist is small, there are finite numbers of solutions to the head-orientation that can be computed readily. Once the head orientation is computed, the epipolar geometry of the camera is recovered, leading to solutions to the 3D joint positions. Results are presented on synthetic and real images.},
	keywords = {3D, analysis;, biomechanics;, body, body-centric, camera;, capture, coordinate, coordinates;, detection;, epipolar, equation, estimation;, geometry;, human, image, image;, images;, model-based, models;, MOTION, object, optical, perspective, physiological, polynomial, polynomials;, pose, real, single, synthetic, system;, systems;, torso, tracking;, twist;, uncalibrated},
	doi = {10.1109/CVPR.2004.1315139},
	author = {Parameswaran, V. and Chellapa, Rama}
}
@conference {12725,
	title = {Shape and motion driven particle filtering for human body tracking},
	booktitle = {Multimedia and Expo, 2003. ICME {\textquoteright}03. Proceedings. 2003 International Conference on},
	volume = {3},
	year = {2003},
	month = {2003/07//},
	pages = {III - 61-4 vol.3 - III - 61-4 vol.3},
	abstract = {In this paper, we propose a method to recover 3D human body motion from a video acquired by a single static camera. In order to estimate the complex state distribution of a human body, we adopt the particle filtering framework. We present the human body using several layers of representation and compose the whole body step by step. In this way, more effective particles are generated and ineffective particles are removed as we process each layer. In order to deal with the rotational motion, the frequency of rotation is obtained using a preprocessing operation. In the preprocessing step, the variance of the motion field at each image is computed, and the frequency of rotation is estimated. The estimated frequency is used for the state update in the algorithm. We successfully track the movement of figure skaters in TV broadcast image sequence, and recover the 3D shape and motion of the skater.},
	keywords = {3D, body, broadcast, camera;, cameras;, estimation;, Filtering, framework;, human, image, MOTION, motion;, particle, processing;, rotational, sequence;, sequences;, signal, single, static, theory;, tracking;, TV, video},
	doi = {10.1109/ICME.2003.1221248},
	author = {Yamamoto, T. and Chellapa, Rama}
}
