Target-driven video summarization in a camera network
Journal
2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings
Pages
3577-3581
Date Issued
2013
Author(s)
Abstract
Nowadays, ever expanding camera network makes it difficult to find the suspect from lengthy video records. This paper proposes a target-driven video summarization framework which provides two-step Filtered Summarized Video (FSV) for tracing suspects. Before the target is identified, users can find the target efficiently using the firststep FSV of any arbitrary camera. The first-step FSV filters all the attributes of the target including the time information and the target's categories. After identifying the target, the second-step FSV with additional spatio-temporal & appearance cues are triggered in the neighbor cameras. To enhance the accuracy of the object classification for FSV, we propose a Perspective Dependent Model (PDM) which consists of many grid-based models. Finally, the experimental results show that grid-based model is more robust than general detectors and the user study demonstrates better performance for target finding and tracking in camera network for surveillance. © 2013 IEEE.
Subjects
camera network; object classification; video summarization; video surveillance
SDGs
Other Subjects
Cameras; Video recording; Camera network; Grid based models; Object classification; Spatio temporal; Target finding; Time information; Video summarization; Video surveillance; Security systems
Type
conference paper