Inference of Complex Trajectories by Means of a Multibehavior and Multiobject Tracking Algorithm

Visual tracking of multiple objects is a fundamental aspect of many video-based systems. Today, there are reliable algorithms that can track a small number of objects in restricted situations. However, the tracking of a large number of objects in uncontrolled situations involving interacting objects with complex dynamics is still a challenge. In this situation, the typical assumptions of linearity and independence of object motions are not fulfilled, causing a low tracking performance. This paper proposes a novel Bayesian tracking algorithm for interacting objects that are able to reliably simulate several object behaviors with an uncalibrated camera, which can be positioned in an arbitrary perspective. Three different models of object behavior are used to simulate and predict the object dynamics, where the proportion of hypotheses of each possible behavior of an object depends on the dynamics (position, velocity, etc.) of the other objects in the scene. Experimental results on public databases prove the reliability and robustness of the proposed tracking algorithm in the presence of object interactions.