Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
Real-Time Simultaneous Localisation and Mapping with a Single Camera
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
Guided-MLESAC: Faster Image Transform Estimation by Using Matching Priors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active Search for Real-Time Vision
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
KALMANSAC: Robust Filtering by Consensus
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
MonoSLAM: Real-Time Single Camera SLAM
IEEE Transactions on Pattern Analysis and Machine Intelligence
Parallel Tracking and Mapping for Small AR Workspaces
ISMAR '07 Proceedings of the 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality
Active matching for visual tracking
Robotics and Autonomous Systems
Journal of Field Robotics - Visual Mapping and Navigation Outdoors
Divide and Conquer: EKF SLAM in
IEEE Transactions on Robotics
Large-Scale 6-DOF SLAM With Stereo-in-Hand
IEEE Transactions on Robotics
Inverse Depth Parametrization for Monocular SLAM
IEEE Transactions on Robotics
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Video sequences of real-world situations are often difficult to track with machine vision. Scenes frequently contain visual clutter, repetitive textures and occlusions that make online visual feature tracking difficult. If the camera is allowed to shake or moving objects are present, the exponential search-space of potential feature matches rapidly becomes intractable for real-time applications. In this paper we introduce "Jointly Compatible Pair Linking" (JCPL), an algorithm that efficiently and deterministically identifies the most globally consensual set of feature-measurement matches in tracking problems with probabilistic priors. We demonstrate JCPL as part of a two-stage visual tracking algorithm, showing it correctly resolving significant matching ambiguities in sequences with highly dynamic camera motion while robustly ignoring moving scene objects. In these experiments we show JCPL and the two-stage tracker evaluating a fixed number of tests in an exponential search-space. In one experiment JCPL tested less than 1/200th of the total search space and executed 4.6 times faster than the current gold-standard algorithm "Joint Compatibility Branch and Bound" (JCBB). Given highly ambiguous sequences we show JCPL tracking successfully while standard JCBB chooses incorrect matches and fails. Throughout our experiments the number of costly image matching operations are minimised, where in a typical sequence only 20.4% of the full image matching operations are required.