We proposed a variational Bayesian model for time varying number of person tracking.Our tracker exploit cluttered visual observations obtained from person detectors.People entering and leaving scene are handled with birth and visibility processes.Competitive results are achieved over on classical tracking benchmarks datasets. Display Omitted Object tracking is an ubiquitous problem that appears in many applications such as remote sensing, audio processing, computer vision, human-machine interfaces, human-robot interaction, etc. Although thoroughly investigated in computer vision, tracking a time-varying number of persons remains a challenging open problem. In this paper, we propose an on-line variational Bayesian model for multi-person tracking from cluttered visual observations provided by person detectors. The paper has the following contributions. We propose a variational Bayesian framework for tracking an unknown and varying number of persons. Our model results in a variational expectation-maximization (VEM) algorithm with closed-form expressions both for the posterior distributions of the latent variables and for the estimation of the model parameters. The proposed model exploits observations from multiple detectors, and it is therefore multimodal by nature. Finally, we propose to embed both object-birth and object-visibility processes in an effort to robustly handle temporal appearances and disappearances. Evaluated on classical multiple person tracking datasets, our method shows competitive results with respect to state-of-the-art multiple-object tracking algorithms, such as the probability hypothesis density (PHD) filter, among others.
Generally speaking, object tracking is the temporal estimation of the object's kinematic state. In the context of image-based tracking, the object state is typically a parametrization of its localization in the (2D) image plane. In computer vision, object tracking has been thoroughly investigated @cite_10 . Objects of interest could be people, faces, hands, vehicles, etc. According to the considered number of objects to be tracked, tracking can be classified into single-object tracking, fixed-number multi-object tracking, and varying-number multi-object tracking.
@cite_10
1995903777
We present a vision based control strategy for tracking and following objects using an Unmanned Aerial Vehicle. We have developed an image based visual servoing method that uses only a forward looking camera for tracking and following objects from a multi-rotor UAV, without any dependence on GPS systems. Our proposed method tracks a user specified object continuously while maintaining a fixed distance from the object and also simultaneously keeping it in the center of the image plane. The algorithm is validated using a Parrot AR Drone 2.0 in outdoor conditions while tracking and following people, occlusions and also fast moving objects; showing the robustness of the proposed systems against perturbations and illumination changes. Our experiments show that the system is able to track a great variety of objects present in suburban areas, among others: people, windows, AC machines, cars and plants.