Contextual Information for Applications in Video Surveillance
| dc.contributor.advisor | Shah, Shishir Kirit | |
| dc.contributor.committeeMember | Subhlok, Jaspal | |
| dc.contributor.committeeMember | Gabriel, Edgar | |
| dc.contributor.committeeMember | Prasad, Saurabh | |
| dc.creator | Wei, Li 1988- | |
| dc.date.accessioned | 2019-11-13T02:35:51Z | |
| dc.date.available | 2019-11-13T02:35:51Z | |
| dc.date.created | December 2016 | |
| dc.date.issued | 2016-12 | |
| dc.date.submitted | December 2016 | |
| dc.date.updated | 2019-11-13T02:35:52Z | |
| dc.description.abstract | With a growing network of cameras being used for security applications, video-based monitoring relying on human operators is ineffective and lacking in reliability and scalability. In this thesis, I present automatic solutions that enable monitoring of humans in videos, such as identifying same individuals across different cameras (human re-identification) and recognizing human activities. Analyzing videos using only individual-based features can be very challenging because of the significant appearance and motion variance due to the changing viewpoints, different lighting conditions, and occlusions. Motivated by the fact that people often form groups, it is feasible to model the interaction among group members to disambiguate the individual features in video analysis tasks. This thesis introduces features that leverage the human group as contextual information and demonstrates its performance for the tasks of human re-identification and activity recognition. Two descriptors are introduced for human re-identification. The Subject Centric Group (SCG) feature captures a person’s group appearance and shape information using the estimate of persons' positions in 3D space. The metric is designed to consider both human appearance and group similarity. The Spatial Appearance Group (SAG) feature extracts group appearance and shape information directly from video frames. A random-forest model is trained to predict the group's similarity score. For human activity recognition, I propose context features along with a deep model to recognize the individual subject’s activity in videos of real-world scenes. Besides the motion features of the person, I also utilize group context information and scene context information to improve the recognition performance. This thesis demonstrates the application of proposed features in both problems. Our experiments show that proposed features can reach state-of-the-art accuracy on challenging re-identification datasets that represent real-world scenario, and can also outperform state-of-the art human activity recognition methods on 5-activities and 6-activities versions of the Collective Activities dataset. | |
| dc.description.department | Computer Science, Department of | |
| dc.format.digitalOrigin | born digital | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.citation | Portions of this document appear in: Wei, Li, and Shishir K. Shah. "Subject centric group feature for person re-identification." In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 28-35. 2015. And in: Wei, Li, and Shishir K. Shah. "Person re-identification with spatial appearance group feature." In 2016 IEEE Symposium on Technologies for Homeland Security (HST), pp. 1-6. IEEE, 2016. | |
| dc.identifier.uri | https://hdl.handle.net/10657/5394 | |
| dc.language.iso | eng | |
| dc.rights | The author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. UH Libraries has secured permission to reproduce any and all previously published materials contained in the work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s). | |
| dc.subject | Context Information | |
| dc.subject | Person re-identification | |
| dc.subject | Activity recognition | |
| dc.subject | Video analytics | |
| dc.title | Contextual Information for Applications in Video Surveillance | |
| dc.type.dcmi | Text | |
| dc.type.genre | Thesis | |
| thesis.degree.college | College of Natural Sciences and Mathematics | |
| thesis.degree.department | Computer Science | |
| thesis.degree.discipline | Computer Science | |
| thesis.degree.grantor | University of Houston | |
| thesis.degree.level | Doctoral | |
| thesis.degree.name | Doctor of Philosophy |