Performance of optical flow techniques
International Journal of Computer Vision
Robust Tracking of Position and Velocity With Kalman Snakes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Vision for Mobile Robot Navigation: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robot Vision
Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
Tracking a Person with 3-D Motion by Integrating Optical Flow and Depth
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Calculating Dense Disparity Maps from Color Stereo Images, an Efficient Implementation
SMBV '01 Proceedings of the IEEE Workshop on Stereo and Multi-Baseline Vision (SMBV'01)
IEEE Transactions on Pattern Analysis and Machine Intelligence
A variational framework for image segmentation combining motion estimation and shape regularization
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
A survey on visual surveillance of object motion and behaviors
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Digital image translational and rotational motion stabilization using optical flow technique
IEEE Transactions on Consumer Electronics
Out-of-sequence measurements fusion for asynchronous multisensor network
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Target tracking for mobile robot platforms via object matching and background anti-matching
Robotics and Autonomous Systems
Visual object tracking by an evolutionary self-organizing neural network
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Evolutionary neural networks for practical applications
Evolving a self-organizing feature map for visual object tracking
WSOM'11 Proceedings of the 8th international conference on Advances in self-organizing maps
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In this paper, a novel algorithm is proposed for the visual target tracking by Autonomous Guide Vehicles (AGV). This paper proposes a sensor data fusion system to estimate the dynamics of the target. Optical flow vectors, colour features, stereo pair disparities are used as the visual features while the vehicle's inertial measurements are used to estimate the stereo cameras' motion. The algorithm estimates the velocity and position of the target which is then used by the vehicle to track the target. In this sensor data fusion-based tracking system, the measurements from the same target can arrive out of sequence. This is called the ''Out-Of-Sequence'' Measurements (OOSM) problem. Thus the resulting problem - how to update the current state estimate with an ''older'' measurement - needs to be solved. In this paper the 1-step-lag OOSM solution from Bar-Shalom is applied for the Extended Kalman Filter-based target-state estimation. The performance of the proposed tracking algorithm with the OOSM solution is demonstrated through extensive experimental results.