CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
An Integrated Bayesian Approach to Layer Extraction from Image Sequences
IEEE Transactions on Pattern Analysis and Machine Intelligence
Video flashlights: real time rendering of multiple videos for immersive model visualization
EGRW '02 Proceedings of the 13th Eurographics workshop on Rendering
Smart Cameras as Embedded Systems
Computer
Color-Based Probabilistic Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Towards Robust Multi-cue Integration for Visual Tracking
ICVS '01 Proceedings of the Second International Workshop on Computer Vision Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Counting People in Crowds with a Real-Time Network of Simple Image Sensors
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Motion Layer Extraction in the Presence of Occlusion Using Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distributed Interactive Video Arrays for Event Capture and Enhanced Situational Awareness
IEEE Intelligent Systems
Adaptive Probabilistic Tracking Embedded in a Smart Camera
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Image and Vision Computing
3D target tracking in distributed smart camera networks with in-network aggregation
Proceedings of the Fourth ACM/IEEE International Conference on Distributed Smart Cameras
Self-organizing computer vision for robust object tracking in smart cameras
ATC'10 Proceedings of the 7th international conference on Autonomic and trusted computing
Thermal-aware sensor scheduling for distributed estimation
ACM Transactions on Sensor Networks (TOSN)
Hi-index | 0.00 |
Tracking applications based on distributed and embedded sensor networks are emerging today, both in the fields of surveillance and industrial vision. Traditional centralized approaches have several drawbacks, due to limited communication bandwidth, computational requirements, and thus limited spatial camera resolution and frame rate. In this article, we present network-enabled smart cameras for probabilistic tracking. They are capable of tracking objects adaptively in real time and offer a very bandwidthconservative approach, as the whole computation is performed embedded in each smart camera and only the tracking results are transmitted, which are on a higher level of abstraction. Based on this, we present a distributed surveillance system. The smart cameras' tracking results are embedded in an integrated 3D environment as live textures and can be viewed from arbitrary perspectives. Also a georeferenced live visualization embedded in Google Earth is presented.