VS '98 Proceedings of the 1998 IEEE Workshop on Visual Surveillance
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Automatic Camera Selection and Fusion for Outdoor Surveillance under Changing Weather Conditions
AVSS '03 Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance
Detecting Pedestrians Using Patterns of Motion and Appearance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
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
Acquiring Multi-Scale Images by Pan-Tilt-Zoom Control and Automatic Multi-Camera Calibration
WACV-MOTION '05 Proceedings of the Seventh IEEE Workshops on Application of Computer Vision (WACV/MOTION'05) - Volume 1 - Volume 01
Pre-Attentive Face Detection for Foveated Wide-Field Surveillance
WACV-MOTION '05 Proceedings of the Seventh IEEE Workshops on Application of Computer Vision (WACV/MOTION'05) - Volume 1 - Volume 01
Surveillance camera scheduling: a virtual vision approach
Proceedings of the third ACM international workshop on Video surveillance & sensor networks
Multi-view-based Cooperative Tracking of Multiple Human Objects in Cluttered Scenes
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Evaluation campaigns and TRECVid
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Towards on-line saccade planning for high-resolution image sensing
Pattern Recognition Letters
Sensor Scheduling for Optimal Observability Using Estimation Entropy
PERCOMW '07 Proceedings of the Fifth IEEE International Conference on Pervasive Computing and Communications Workshops
Detection and tracking of humans and faces
Journal on Image and Video Processing - Regular
An occlusion metric for selecting robust camera configurations
Machine Vision and Applications
Object and Scene-Centric Activity Detection Using State Occupancy Duration Modeling
AVSS '08 Proceedings of the 2008 IEEE Fifth International Conference on Advanced Video and Signal Based Surveillance
Multi-feature graph-based object tracking
CLEAR'06 Proceedings of the 1st international evaluation conference on Classification of events, activities and relationships
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Optimizing Multiple Object Tracking and Best View Video Synthesis
IEEE Transactions on Multimedia
Consistent labeling of tracked objects in multiple cameras with overlapping fields of view
IEEE Transactions on Pattern Analysis and Machine Intelligence
Topic based photo set retrieval using user annotated tags
Multimedia Tools and Applications
Coverage quality and smoothness criteria for online view selection in a multi-camera network
ACM Transactions on Sensor Networks (TOSN)
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We present a content-aware multi-camera selection technique that uses object- and frame-level features. First objects are detected using a color-based change detector. Next trajectory information for each object is generated using multi-frame graph matching. Finally, multiple features including size and location are used to generate an object score. At frame-level, we consider total activity, event score, number of objects and cumulative object score. These features are used to generate score information using a multivariate Gaussian distribution. The algorithm. The best view is selected using a Dynamic Bayesian Network (DBN), which utilizes camera network information. DBN employs previous view information to select the current view thus increasing resilience to frequent switching. The performance of the proposed approach is demonstrated on three multi-camera setups with semi-overlapping fields of view: a basketball game, an indoor airport surveillance scenario and a synthetic outdoor pedestrian dataset. We compare the proposed view selection approach with a maximum score based camera selection criterion and demonstrate a significant decrease in camera flickering. The performance of the proposed approach is also validated through subjective testing.