Robust Object Matching for Persistent Tracking with Heterogeneous Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Viewpoint Invariant Pedestrian Recognition with an Ensemble of Localized Features
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Moving object verification in airborne video sequences
IEEE Transactions on Circuits and Systems for Video Technology
Vehicle tracking based on image alignment in aerial videos
EMMCVPR'07 Proceedings of the 6th international conference on Energy minimization methods in computer vision and pattern recognition
Incremental unsupervised three-dimensional vehicle model learning from video
IEEE Transactions on Intelligent Transportation Systems
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Visual recognition of objects through multiple observations is an important component of object tracking. We address the problem of vehicle matching when multiple observations of a vehicle are separated in time such that frames of observations are not contiguous, thus prohibiting the use of standard frame-to-frame data association. We employ features extracted over a sequence during one time interval as a vehicle fingerprint that is used to compute the likelihood that two or more sequence observations are from the same or different vehicles. The challenges of change in pose, aspect and appearances across two disparate observations are handled by combining feature-based quasi-rigid alignment with flexible matching between two or more sequences. The current work uses the domain of vehicle tracking from aerial platforms where typically both the imaging platform and the vehicles are moving and the number of pixels on the object are limited to fairly low resolutions. Extensive evaluation with respect to ground truth is reported in the paper.