QTRAJECTORIES: IMPROVING THE QUALITY OF OBJECT TRACKING USING SELF-ORGANIZING CAMERA NETWORKS
Previous work in the research field of video surveillance intensively focused on separated aspects of object detection, data association, pattern recognition and system design. In contrast, we propose a holistic approach for object tracking in a self-organizing and distributed smart camera network....
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2012-07-01
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| Series: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/I-4/269/2012/isprsannals-I-4-269-2012.pdf |
| Summary: | Previous work in the research field of video surveillance intensively focused on separated aspects of object detection, data association,
pattern recognition and system design. In contrast, we propose a holistic approach for object tracking in a self-organizing and distributed
smart camera network. Each observation task is represented by a software-agent which improves the tracking performance by
collaborative behavior. An object tracking agent detects persons in a video stream and associates them with a trajectory. The pattern
recognition agent analyses these trajectories by detecting points of interest within the observation field. These are characterized by a
non-deterministic behavior of the moving person. The trajectory points (enriched by the results of the pattern recognition agent) will
be used by a configuration agent to align the cameras field of view. We show that this collaboration improves the performance of the
observation system by increasing the amount of detected trajectory points by 22%. |
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| ISSN: | 2194-9042 2194-9050 |