Pattern Recognition Letters
Object Matching in Distributed Video Surveillance Systems by LDA-Based Appearance Descriptors
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
Contradiction and Correlation for Camera Overlap Estimation
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Analysis of appearance features for human matching between different fields of view
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part II
Mutual calibration of camera motes and RFIDs for people localization and identification
Proceedings of the Fourth ACM/IEEE International Conference on Distributed Smart Cameras
Adaptive color transformation for person re-identification in camera networks
Proceedings of the Fourth ACM/IEEE International Conference on Distributed Smart Cameras
Learning non-coplanar scene models by exploring the height variation of tracked objects
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
Detecting anomalies in people's trajectories using spectral graph analysis
Computer Vision and Image Understanding
Person re-identification in crowd
Pattern Recognition Letters
A fast multi-scale covariance descriptor for object re-identification
Pattern Recognition Letters
An adaptive sample count particle filter
Computer Vision and Image Understanding
People reidentification in surveillance and forensics: A survey
ACM Computing Surveys (CSUR)
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Tracking single individuals as they move across disjoint camera views is a challenging task since their appearance may vary significantly between views. Major changes in appearance are due to different and varying illumination conditions and the deformable geometry of people. These effects are hard to estimate and take into account in real-life applications. Thus, in this paper we propose an illumination-tolerant appearance representation, which is capable of coping with the typical illumination changes occurring in surveillance scenarios. The appearance representation is based on an online k-means colour clustering algorithm, a data-adaptive intensity transformation and the incremental use of frames. A similarity measurement is also introduced to compare the appearance representations of any two arbitrary individuals. Post-matching integration of the matching decision along the individuals‘ tracks is performed in order to improve reliability and robustness of matching. Once matching is provided for any two views of a single individual, its tracking across disjoint cameras derives straightforwardly. Experimental results presented in this paper from a real surveillance camera network show the effectiveness of the proposed method.