Several applications based on visual tracking need a better accuracy to perform a more reliable analysis of the objects in scene. However, it is necessary to deal with environments with different atmospheric conditions. Object dynamics can affect tracking throughout time. In this work, a tracking method with subpixel measurements is described, where quality of the state estimate of the object is enhanced. The proposed scheme is robust in scenes with occlusions and changes in appearance of the target. The target model is adapted to size changes of the object, avoiding aperture problem and integration with false information. The state of the object and its aspect along time are estimated. Each pixel is modeled by a random variable because the set of pixels represents the non-observable surface of target where real value of pixels can be affected by noise. This assumption allows the design of a gradual scheme for model updating. Subpixel precision in tracking is based on an iterative method that uses the similitude surface between the target model and the current image of the object on tracking.

This paper compares the performance of adaptive trackers based on multiple algorithms. The aim of using multiple algorithms is to increase the robustness of the trackers under varying conditions. We perform two estimation algorithms UKF and IMM to measure the performance of tracking on outdoor scenes with occlusions. The purpose of this paper is to measure and evaluate tracker reliability for be able to determine the position of a target. The performance is evaluated using metrics related to truth track. We give a positional evaluation and statistics values of the performance of visual tracking systems, which adapt to changing environments.