Adaptive sizing tracking based on probabilistic methods
Héctor Barrón, Janeth Cruz-Enriquez, Leopoldo Altamirano-Robles · International Conference on Signal Processing · 2006
In this paper, it is presented an adaptive approach for tracking that is strengthened with probabilistic methods to estimate the correct state of the object. Most of the tracking approaches are focused only on dynamic (temporal) behaviors of the target. However, this method is also adaptive to spatial characteristics of the target such as its size, due the size and the shape of the object can change over time. To estimate the target position, a Probabilistic Markov Model as Kalman Filter is used, and to estimate the object appearance, the same method is used for defining a search space with random samples. Furthermore, the method maintains the tracking even when the target is affected by occlusion or noisy background. Tracking performance is tested with synthetic and real image sequences and we present precision and accuracy under different conditions.