Classification-Based Likelihood Functions for Bayesian Tracking

TL;DR: A general framework for learning probabilistic models of objects for exploiting these models for tracking objects in image sequences is described, which uses a discriminative classifier to learn models of how they appear in images.

Abstract: The success of any Bayesian particle filtering based tracker relies heavily on the ability of the likelihood function to discriminate between the state that fits the image well and those that do not This paper describes a general framework for learning probabilistic models of objects for exploiting these models for tracking objects in image sequences We use a discriminative classifier to learn models of how they appear in images In particular, we use a support vector machine (SVM) for training, which is able to extract useful non-linear information, and thus represent more complex characteristics of the tracked object and background This is a particular advantage when tracking deformable objects and where appearance changes due to the unstable illumination and pose occur A by-product of the SVM training procedure is the classification function, with which the tracking problem is cast into a binary classification problem An object detector directly using the classification function is then available To make the tracker robust, an object detector that directly uses the classification function is combined into the tracker for object verification This provides the capability for automatic initialisation and recovery from momentary tracking failures We demonstrate improved robustness in image sequences