The Journal of Machine Learning Research
Tracking Across Multiple Cameras With Disjoint Views
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
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
Using Multiple Segmentations to Discover Objects and their Extent in Image Collections
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Multimedia surveillance: content-based retrieval with multicamera people tracking
Proceedings of the 4th ACM international workshop on Video surveillance and sensor networks
Tracking people across disjoint camera views by an illumination-tolerant appearance representation
Machine Vision and Applications
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
HECOL: Homography and epipolar-based consistent labeling for outdoor park surveillance
Computer Vision and Image Understanding
Pattern Recognition Letters
Correspondence-Free Activity Analysis and Scene Modeling in Multiple Camera Views
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Entropy-based localization of textured regions
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing: Part I
An study on re-identification in RGB-D imagery
IWAAL'12 Proceedings of the 4th international conference on Ambient Assisted Living and Home Care
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Establishing correspondences among object instances is still challenging in multi-camera surveillance systems, especially when the cameras' fields of view are non-overlapping. Spatiotemporal constraints can help in solving the correspondence problem but still leave a wide margin of uncertainty. One way to reduce this uncertainty is to use appearance information about the moving objects in the site. In this paper we present the preliminary results of a new method that can capture salient appearance characteristics at each camera node in the network. A Latent Dirichlet Allocation (LDA) model is created and maintained at each node in the camera network. Each object is encoded in terms of the LDA bag-of-words model for appearance. The encoded appearance is then used to establish probable matching across cameras. Preliminary experiments are conducted on a dataset of 20 individuals and comparison against Madden's I-MCHR is reported.