text: Data fusion has become an active research topic in recent years. Growing computational performance has allowed the use of redundant sensors to measure a single phenomenon. While Bayesian fusion approaches are common in general applications, the computer vision field has largely relegated this approach. Most object following algorithms have gone towards pure machine learning fusion techniques that tend to lack flexibility. Consequently, a more general data fusion scheme is needed. Within this work, a hierarchical Bayesian fusion approach is proposed, which outperforms individual trackers by using redundant measurements. The adaptive framework is achieved by relying on each measurement's local statistics and a global softened majority voting. The proposed approach was validated in a simulated application and two robotic platforms.
  @cite_0: 
  @cite_15: The motivation of this research is to show that visual based object tracking and following is reliable using a cheap GPS-denied multirotor platform such as the AR Drone 2.0. Our architecture allows the user to specify an object in the image that the robot has to follow from an approximate constant distance. At the current stage of our development, in the event of image tracking loss the system starts to hover and waits for the image tracking recovery or second detection, which requires the usage of odometry measurements for self stabilization. During the following task, our software utilizes the forward-facing camera images and part of the IMU data to calculate the references for the four on-board low-level control loops. To obtain a stronger wind disturbance rejection and an improved navigation performance, a yaw heading reference based on the IMU data is internally kept and updated by our control algorithm. We validate the architecture using an AR Drone 2.0 and the OpenTLD tracker in outdoor suburban areas. The experimental tests have shown robustness against wind perturbations, target occlusion and illumination changes, and the system's capability to track a great variety of objects present on suburban areas, for instance: walking or running people, windows, AC machines, static and moving cars and plants.
  @cite_32: There has been an ever-increasing interest in multi-disciplinary research on multisensor data fusion technology, driven by its versatility and diverse areas of application. Therefore, there seems to be a real need for an analytical review of recent developments in the data fusion domain. This paper proposes a comprehensive review of the data fusion state of the art, exploring its conceptualizations, benefits, and challenging aspects, as well as existing methodologies. In addition, several future directions of research in the data fusion community are highlighted and described.
  @cite_3: We propose two model-free visual object trackers for following targets using the low-cost quadrotor Parrot AR.Drone 2.0 at low altitudes. Our trackers employ correlation filters for short-term tracking and a redetection strategy based on tracking-learning-detection (TLD). We performed an extensive quantitative evaluation of our trackers and a wide variety of existing trackers on person pursuit sequences. The results show that our trackers outperform the existing trackers. In addition, we demonstrate the applicability of our proposed trackers in a series of flight experiments in unconstrained environments using human targets and an existing visual servoing controller.
summary: This work aims to create a general Bayesian approach for real-time applications in robotic platforms. The proposed method processes the bounding boxes of the trackers detectors as sensor measurements. This framework is founded in the basis of the bank of KFs with some similarities with the mixture of experts aforecited. Furthermore, this scheme addresses some common problems such as data imperfection, outliers and spurious data, measurement delays, static vs. dynamic phenomena, and others discussed in @cite_32 . Additionally, this approach was tested in simulated signals and two different robotics platforms: An UAV system and a pan-tilt system. Both are capable of following a target. While similar approaches have used vision-based trackers to control a small UAV in @cite_15 @cite_0 and @cite_3 . Previous works did not consider the fusion of several methods at a bounding box level to improve reliability over longer time spans.