@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 {13121,
	title = {Action recognition using ballistic dynamics},
	booktitle = {Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on},
	year = {2008},
	month = {2008/06//},
	pages = {1 - 8},
	abstract = {We present a Bayesian framework for action recognition through ballistic dynamics. Psycho-kinesiological studies indicate that ballistic movements form the natural units for human movement planning. The framework leads to an efficient and robust algorithm for temporally segmenting videos into atomic movements. Individual movements are annotated with person-centric morphological labels called ballistic verbs. This is tested on a dataset of interactive movements, achieving high recognition rates. The approach is also applied on a gesture recognition task, improving a previously reported recognition rate from 84\% to 92\%. Consideration of ballistic dynamics enhances the performance of the popular Motion History Image feature. We also illustrate the approachpsilas general utility on real-world videos. Experiments indicate that the method is robust to view, style and appearance variations.},
	keywords = {analysis;image, Bayesian, dynamics;gesture, feature;person-centric, framework;action, History, image, labels;psycho-kinesiological, morphological, MOTION, Movement, movements;motion, planning;interactive, processing;, recognition, recognition;ballistic, recognition;image, segmentation;video, signal, studies;image, task;human},
	doi = {10.1109/CVPR.2008.4587806},
	author = {Vitaladevuni,S.N. and Kellokumpu,V. and Davis, Larry S.}
}
