Object Tracking and Mensuration in Surveillance Videos
| dc.contributor.advisor | Chellappa, Rama | en_US |
| dc.contributor.author | Shao, Jie | en_US |
| dc.contributor.department | Electrical Engineering | en_US |
| dc.contributor.publisher | Digital Repository at the University of Maryland | en_US |
| dc.contributor.publisher | University of Maryland (College Park, Md.) | en_US |
| dc.date.accessioned | 2010-07-02T05:50:15Z | |
| dc.date.available | 2010-07-02T05:50:15Z | |
| dc.date.issued | 2010 | en_US |
| dc.description.abstract | This thesis focuses on tracking and mensuration in surveillance videos. The first part of the thesis discusses several object tracking approaches based on the different properties of tracking targets. For airborne videos, where the targets are usually small and with low resolutions, an approach of building motion models for foreground/background proposed in which the foreground target is simplified as a rigid object. For relatively high resolution targets, the non-rigid models are applied. An active contour-based algorithm has been introduced. The algorithm is based on decomposing the tracking into three parts: estimate the affine transform parameters between successive frames using particle filters; detect the contour deformation using a probabilistic deformation map, and regulate the deformation by projecting the updated model onto a trained shape subspace. The active appearance Markov chain (AAMC). It integrates a statistical model of shape, appearance and motion. In the AAMC model, a Markov chain represents the switching of motion phases (poses), and several pairwise active appearance model (P-AAM) components characterize the shape, appearance and motion information for different motion phases. The second part of the thesis covers video mensuration, in which we have proposed a heightmeasuring algorithm with less human supervision, more flexibility and improved robustness. From videos acquired by an uncalibrated stationary camera, we first recover the vanishing line and the vertical point of the scene. We then apply a single view mensuration algorithm to each of the frames to obtain height measurements. Finally, using the LMedS as the cost function and the Robbins-Monro stochastic approximation (RMSA) technique to obtain the optimal estimate. | en_US |
| dc.identifier.uri | http://hdl.handle.net/1903/10303 | |
| dc.subject.pqcontrolled | Engineering, Electronics and Electrical | en_US |
| dc.subject.pquncontrolled | height mensuration | en_US |
| dc.subject.pquncontrolled | object tracking | en_US |
| dc.subject.pquncontrolled | particle filter | en_US |
| dc.subject.pquncontrolled | statistical | en_US |
| dc.subject.pquncontrolled | surveillance video | en_US |
| dc.title | Object Tracking and Mensuration in Surveillance Videos | en_US |
| dc.type | Dissertation | en_US |
Files
Original bundle
1 - 1 of 1