Maximum Likelihood Estimation while the object performs typical movements. The observation model cannot be directly estimated from the data, requiring assumptions to be made in order to estimate it. Isard 1998 assumes that the clutter which may make the object not visible is a Poisson random process with spatial density and that any true target measurement is unbiased and normally distributed with standard deviation . The basic condensation algorithm is used to track a single object in time. It is possible to extend the condensation algorithm using a single probability distribution to describe the likely states of multiple objects to track multiple objects in a scene at the same time. Since clutter can cause the object probability distribution to split into multiple peaks, each peak represents a hypothesis about the object configuration. Smoothing is a statistical technique of conditioning the distribution based on both past and future measurements once the tracking is complete in order to reduce the effects of multiple peaks. Smoothing cannot be directly done in real-time since it requires information of future measurements. Applications The algorithm can be used for vision-based robot localization of mobile robots. Instead of tracking the position of an object in the scene, however, the position of the camera platform is tracked. This allows the camera platform to be globally localized given a visual map of the environment. Extensions of the condensation algorithm have also been used to recognize human gestures in image sequences. This application of the condensation algorithm impacts the range of human–computer interactions possible. It has been used to recognize simple gestures of a user at a whiteboard to control actions such as selecting regions of the boards to print or save them. Other extensions have also been used for tracking multiple cars in the same scene. The condensation algorithm has also been used for face recognition in a video sequence. Resources An implementation of the condensation algorithm in C can be found on Michael Isard’s website. An implementation in MATLAB can be found on the Mathworks File Exchange. An example of implementation using the OpenCV library can be found on the OpenCV forums. See also Particle filter – Condensation is the application of Sampling Importance Resampling (SIR) estimation to contour tracking References Computer vision