Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Exact indexing of dynamic time warping
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Trajectory Association across Multiple Airborne Cameras
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object identification in a Bayesian context
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Multi-feature graph-based object tracking
CLEAR'06 Proceedings of the 1st international evaluation conference on Classification of events, activities and relationships
A stochastic approach to tracking objects across multiple cameras
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Exploiting multiple cameras for environmental pathlets
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part III
Iterative hypothesis testing for multi-object tracking with noisy/missing appearance features
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume 2
Low-complexity scalable distributed multicamera tracking of humans
ACM Transactions on Sensor Networks (TOSN)
People reidentification in surveillance and forensics: A survey
ACM Computing Surveys (CSUR)
Key observation selection-based effective video synopsis for camera network
Machine Vision and Applications
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We present a novel unsupervised inter-camera trajectory correspondence algorithm that does not require prior knowledge of the camera placement. The approach consists of three steps, namely association, fusion and linkage. For association, local trajectory pairs corresponding to the same physical object are estimated using multiple spatio-temporal features on a common ground-plane. To disambiguate spurious associations, we employ a hybrid approach that utilizes the matching results on the image- and ground-plane. The trajectory segments after association are fused by adaptive averaging. Finally, linkage integrates segments and generates a single trajectory of an object across the entire observed area. We evaluated the performance of the proposed approach on a simulated and two real scenarios with simultaneous moving objects observed by multiple cameras and compared it with state-of-the-art algorithms. Convincing results are observed in favor of the proposed approach.