IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Illumination insensitive recognition using eigenspaces
Computer Vision and Image Understanding
Appearance Modeling for Tracking in Multiple Non-Overlapping Cameras
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Vehicle Identification between Non-Overlapping Cameras without Direct Feature Matching
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Robust Object Matching for Persistent Tracking with Heterogeneous Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
IEEE Transactions on Pattern Analysis and Machine Intelligence
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Object identification in a Bayesian context
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Computer Vision and Image Understanding
Real-time multiple vehicle detection and tracking from a moving vehicle
Machine Vision and Applications
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Tracking vehicles using a network of cameras with non-overlapping views is a challenging problem of great importance in traffic surveillance. One of the main challenges is accurate vehicle matching across the cameras. Even if the cameras have similar views on vehicles, vehicle matching remains a difficult task due to changes of their appearance between observations, and inaccurate detections and occlusions, which often occur in real scenarios. To be executed on smart cameras the matching has also to be efficient in terms of needed data and computations. To address these challenges we present a low complexity method for vehicle matching robust against appearance changes and inaccuracies in vehicle detection. We efficiently represent vehicle appearances using signature vectors composed of Radon transform like projections of the vehicle images and compare them in a coarse-to-fine fashion using a simple combination of 1-D correlations. To deal with appearance changes we include multiple observations in each vehicle appearance model. These observations are automatically collected along the vehicle trajectory. The proposed signature vectors can be calculated in low-complexity smart cameras, by a simple scan-line algorithm of the camera software itself, and transmitted to the other smart cameras or to the central server. Extensive experiments based on real traffic surveillance videos recorded in a tunnel validate our approach.