Hands: a pattern theoretic study of biological shapes
Hands: a pattern theoretic study of biological shapes
Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discrete and Combinatorial Mathematics: An Applied Introduction
Discrete and Combinatorial Mathematics: An Applied Introduction
Tracking Across Multiple Cameras With Disjoint Views
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Robust Real-Time Face Detection
International Journal of Computer Vision
A master-slave system to acquire biometric imagery of humans at distance
IWVS '03 First ACM SIGMM international workshop on Video surveillance
Scheduling an active camera to observe people
Proceedings of the ACM 2nd international workshop on Video surveillance & sensor networks
Inference of Non-Overlapping Camera Network Topology by Measuring Statistical Dependence
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Automatic pan-tilt-zoom calibration in the presence of hybrid sensor networks
Proceedings of the third ACM international workshop on Video surveillance & sensor networks
Similarity-based analysis for large networks of ultra-low resolution sensors
Pattern Recognition
EURASIP Journal on Applied Signal Processing
Calibrating distributed camera networks using belief propagation
EURASIP Journal on Applied Signal Processing
Bridging the gaps between cameras
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Consistent labeling of tracked objects in multiple cameras with overlapping fields of view
IEEE Transactions on Pattern Analysis and Machine Intelligence
Coverage quality and smoothness criteria for online view selection in a multi-camera network
ACM Transactions on Sensor Networks (TOSN)
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Given a hybrid camera layout—one containing, for example, static and active cameras—and people moving around following established traffic patterns, our goal is to predict a subset of cameras, respective camera parameter settings, and future time windows that will most likely lead to success the vision tasks, such as, face recognition when a camera observes an event of interest. We propose an adaptive probabilistic model that accrues temporal camera correlations over time as the cameras report observed events. No extrinsic, intrinsic, or color calibration of cameras is required. We efficiently obtain the camera parameter predictions using a modified Sequential Monte Carlo method. We demonstrate the performance of the model in an example face detection scenario in both simulated and real environment experiments, using several active cameras.