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
Learning and Recognizing Human Dynamics in Video Sequences
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
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
Two-Hand Gesture Recognition using Coupled Switching Linear Model
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
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)
Multi-Modal Face Tracking Using Bayesian Network
AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
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
Image-enhanced multiple model tracking
Automatica (Journal of IFAC)
Digital image translational and rotational motion stabilization using optical flow technique
IEEE Transactions on Consumer Electronics
Note: Low-resolution color-based visual tracking with state-space model identification
Computer Vision and Image Understanding
Multi-Camera Tracking with Adaptive Resource Allocation
International Journal of Computer Vision
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In this paper, a novel algorithm is proposed for the vision-based object tracking by autonomous vehicles. To estimate the velocity of the tracked object, the algorithm fuses the information captured by the vehicle's on-board sensors such as the cameras and inertial motion sensors. Optical flow vectors, color features, stereo pair disparities are used as optical features while the vehicle's inertial measurements are used to determine the cameras' motion. The algorithm determines the velocity and position of the target in the world coordinate which are then tracked by the vehicle. In order to formulate this tracking algorithm, it is necessary to use a proper model which describes the dynamic information of the tracked object. However due to the complex nature of the moving object, it is necessary to have robust and adaptive dynamic models. Here, several simple and basic linear dynamic models are selected and combined to approximate the unpredictable, complex or highly nonlinear dynamic properties of the moving target. With these basic linear dynamic models, a detailed description of the three-dimensional (3D) target tracking scheme using the Interacting Multiple Models (IMM) along with an Extended Kalman Filter is presented. The final state of the target is estimated as a weighted combination of the outputs from each different dynamic model. Performance of the proposed fusion based IMM tracking algorithm is demonstrated through extensive experimental results.