Francisco Madrigal

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This paper describes an original strategy for using a datadriven probabilistic motion model into particle filter-based target tracking on video streams. Such a model is based on the local motion observed by the camera during a learning phase. Given that the initial, empirical distribution may be incomplete and noisy, we regularize it in a second phase. The(More)
Multiple targets tracking is a challenging problem due to occlusions or identity switching. Although the use of prior information about the motion of the targets improves the tracking results, a single motion model may not capture the complex dynamic of the targets. This is a common situation with pedestrians, as each person moves in its own way, making(More)
Even though pedestrian motion may look chaotic in most of the cases, recent studies have shown that this motion is mainly ruled by environment and social aspects. In this paper, we propose an interacting multiple model pedestrian tracking framework that incorporates these semantic considerations as a prior knowledge about intentions and interactions between(More)
We propose a methodology for learning and using a multiple-goal probabilistic motion model within a particle filter-based target tracking on video streams. In a set of training video sequences, we first extract the locations (coined as “goals”) where the pedestrians either leave the scene or often change directions. Then, we learn one motion(More)
This article describes an original strategy for enhancing current state-of-the-art trackers through the use of motion priors, built as data-driven probabilistic motion models for moving targets. Our priors have a simple form and can replace advantageously more traditional models, such as the constant velocity or constant acceleration models, that are of(More)
In this paper, the problem of automated scene understanding by tracking and predicting paths for multiple humans is tackled, with a new methodology using data from a single, fixed camera monitoring the environment. Our main idea is to build goal-oriented prior motion models that could drive both the tracking and path prediction algorithms, based on a(More)
This paper presents a particle filter-based approach for multiple target tracking in video streams in single static cameras settings. We aim in particular to manage mid-dense crowds situations, where, although tracking is possible, it is made complicated by the presence of frequent occlusions among targets and with scene clutter. Moreover, the appearance of(More)
This paper evaluates and compares different hyperparameters optimization tools that can be used in any vision applications for tuning their underlying free parameters. We focus in the problem of multiple object tracking, as it is widely studied in the literature and offers several parameters to tune. The selected tools are freely available or easy to(More)