Farhad Dadgostar
ABSTRACT
In the field of video analytics for surveillance, the trend towards the use of multi-camera and high definition video is increasing. This poses significant technical challenges in terms of video transmission and real-time processing for surveillance analytics, such as people recognition and tracking. Currently, available solutions are typically proprietary commercial systems which are costly to purchase. These proprietary systems also do not facilitate research collaboration across members of the computer vision community. We propose a framework for video analytics research based only on open-source software which is collaborative, scalable, interoperable, and distributed. This framework was successfully applied to the task of face recognition on both live video feeds and video datasets.
- }}A. N. E. Belbachir, Smart Cameras: Springer, 2010.Google Scholar
- }}G. Aggarwal, A. K. R. Chowdhury, and R. Chellappa, "A System Identification Approach for Video-based Face Recognition," in 17th IEEE International Conference on Pattern Recognition (ICPR'04), Cambridge UK, 2004, pp. 175--178. Google ScholarDigital Library
- }}H. Detmold, A. Dick, K. Falkner, D. Munro, van den Hengel, Anton, and R. Morrison, "Scalable Surveillance Software Architecture," in IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Sydney, Australia: IEEE Computer Society, 2006, pp. 103--107. Google ScholarDigital Library
- }}M. H. Sedky, M. Moniri, and C. C. Chibelushi, "Classification of smart video surveillance systems for commercial applications," in IEEE Conference on Advanced Video and Signal Based Surveillance (AVSS): IEEE, 2005, pp. 638--643.Google Scholar
- }}M. Quigley, B. Gerkey, J. Faust, K. Conley, T. Foote, J. Leibs, E. Berger, R. Wheeler, and A. Y. Ng, "ROS: an open-source Robot Operating System," in Open-Source Software workshop at the International Conference on Robotics and Automation (ICRA) Kube, Japan, 2009.Google Scholar
- }}Y.-l. Tian, L. Brown, A. Hampapur, M. Lu, A. Senior, and C.-f. Shu, "IBM smart surveillance system (S3): event based video surveillance system with open and extensible framework," Machine Vision and Applications, pp. 317--327, 2008. Google ScholarDigital Library
Index Terms
- A framework for lab-based real-time video analysis on distributed camera networks
Recommendations
Enhancement of video streaming analysis using cluster-computing framework
AbstractVideo content analysis is an emerging technique to easily redact video footage for public disclosure and to identify events and objects in surveillance cameras. The proficiency of this analysis depends on various crucial parameters such as area ...
Scaling Video Analytics Systems to Large Camera Deployments
HotMobile '19: Proceedings of the 20th International Workshop on Mobile Computing Systems and ApplicationsDriven by advances in computer vision and the falling costs of camera hardware, organizations are deploying video cameras en masse for the spatial monitoring of their physical premises. Scaling video analytics to massive camera deployments, however, ...
Real-time camera pose estimation via line tracking
Real-time camera calibration has been intensively studied in augmented reality. However, for texture-less and texture-repeated scenes as well as poorly illuminated scenes, obtaining a stable calibration is still an open problem. In the paper, we propose ...
Comments