Visual Tracking using the Sum of Conditional Variance (SCV)
Introduction:
In the context of visual tracking, the SCV is invariant to non-linear illumination variations, multimodal and computationally inexpensive. Compared to information theoretic tracking methods, it requires less iterations to converge and has a significantly larger convergence radius. This method is a generalization of the efficient second-order minimization formulation for tracking using the SCV, combining the efficient second-order approximation of the Hessian with a similarity metric invariant to non-linear illumination variations. The result is a visual tracking method that copes with non-linear illumination variations without requiring the estimation of photometric correction parameters at every iteration.
We have recently improved its formulation to cope with extreme illumination variations! Check videos below and our ICIP 2014 paper.
Videos:
Resources:
Click here to access the project page
Publications:
- Richa R., Sznitman R., Taylor R., Hager G., "Visual Tracking Using the Sum of Conditional Variance", (IROS '11).
- Richa R., Souza M., Scandaroli G., Comunello E., von Wangenheim A., "Direct Visual Tracking Under Extreme Illumination Variations using the Sum of Conditional Variance", (ICIP '14).