The Development and Comparison of Robust Methodsfor Estimating the Fundamental Matrix
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Probabilistic Notion of Correspondence and the Epipolar Constraint
3DPVT '06 Proceedings of the Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06)
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
| Hi-index | 0.00 |
Visual tracking with moving cameras is a challenging task. The global motion induced by the moving camera moves the target object outside the expected search area, according to the object dynamics. The typical approach is to use a registration algorithm to compensate the camera motion. However, in situations involving several moving objects, and backgrounds highly affected by the aperture problem, image registration quality may be very low, decreasing dramatically the performance of the tracking. In this work, a novel approach is proposed to successfully tackle the tracking with moving cameras in complex situations, which involve several independent moving objects. The key idea is to compute several hypotheses for the camera motion, instead of estimating deterministically only one. These hypotheses are combined with the object dynamics in a Particle Filter framework to predict the most probable object locations. Then, each hypothetical object location is evaluated by the measurement model using a spatiogram, which is a region descriptor based on color and spatial distributions. Experimental results show that the proposed strategy allows to accurately track an object in complex situations affected by strong ego motion.