Ángel F. García-Fernández

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– This paper addresses the problem of detecting and tracking multiple targets in a Bayesian framework. First, we introduce the definition of Joint MultitracK Probability Density (JMKPD) which is the probability of having a certain number of tracks, each one clearly identified with an ID number, and a kinematic state. We develop the a priori model needed to(More)
—The paper evaluates the error performance of three random finite set based multi-object trackers in the context of pedestrian video tracking. The evaluation is carried out using a publicly available video dataset of 4500 frames (town centre street) for which the ground truth is available. The input to all pedestrian tracking algorithms is an identical set(More)
—This paper describes an algorithm for estimating the road ahead of a host vehicle based on the measurements from several onboard sensors: a camera, a radar, wheel speed sensors, and an inertial measurement unit. We propose a novel road model that is able to describe the road ahead with higher accuracy than the usual polynomial model. We also develop a(More)
—In this paper, we present the generalized optimal sub-pattern assignment (GOSPA) metric on the space of sets of targets. This metric is a generalized version of the unnormalized optimal sub-pattern assignment (OSPA) metric. The difference between unnormalized OSPA and GOSPA is that, in the proposed metric, we can choose a range of values for the(More)
—This paper proposes the set of target trajectories as the state variable for multiple target tracking. The main objective of multiple target tracking is to estimate an unknown number of target trajectories given a sequence of measurements. This quantity of interest is perfectly represented as a set of trajectories without the need of arbitrary parameters(More)