Learning the distribution of object trajectories for event recognition
BMVC '95 Proceedings of the 6th British conference on Machine vision (Vol. 2)
Classification of Unattended and Stolen Objects in Video-Surveillance System
AVSS '06 Proceedings of the IEEE International Conference on Video and Signal Based Surveillance
Intelligent Distributed Video Surveillance Systems (Professional Applications of Computing) (Professional Applications of Computing)
Detection of abandoned objects in crowded environments
AVSS '07 Proceedings of the 2007 IEEE Conference on Advanced Video and Signal Based Surveillance
Feature Extraction & Image Processing, Second Edition
Feature Extraction & Image Processing, Second Edition
Detecting unusual activity in video
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
| Hi-index | 0.00 |
Surveillance video data is accumulating at a staggering rate, making its manual handling impossible. Therefore, automatic tools for analysis and processing of such data are highly needed. In most video surveillance scenarios the most interesting parts of the recorded data are those where an unusual event takes place. The rest of the data, which is actually representing the greatest part, relates to usual or normal activities that are of no real value to the security task and thus its viewing, storage and processing are pure waste of resources. Therefore, automatically finding the exact spot in a surveillance video sequence where an interesting event occurred is of great importance financially and to take timely actions. In this project we investigate the detection of remarkable events in video surveillance scenarios. We look into how to distinguish events in surveillance scenarios, and further what is a remarkable event. We specifically focus our attention on the event of object dropping in public places such as airports and train stations. We try to answer some of the following questions: Is it possible to create a system for modeling salient events in surveillance scenarios? How does one determine what stands out as a remarkable event? How to distinguish between less remarkable events and more remarkable event taking place?