Relative Vessel Motion Tracking using Sensor Fusion, Aruco Markers, and MRU Sensors

Improving feature tracking using motion sensors

Porting state-of-the-art computer vision algorithms to mobile devices is a difficult, but desirable task. Indeed, some applications require mobility, like augmented reality. However, running an application in real time may be tricky given the limited resources available on a mobile device. This is why the algorithms may need to be downscaled or simplified. A typical augmented reality pipeline i...

Short-Term Motion Tracking Using Inexpensive Sensors

Speaker Tracking Using Particle Filter Sensor Fusion

in Proc. of Asian Conference on Computer Vision (ACCV), 2004 Sensor fusion for object tracking has become an active research direction during the past few years. But how to do it in a robust and principled way is still an open problem. In this paper, we propose a new fusion framework that combines both the bottom-up and top-down approaches to probabilistically fuse multiple sensing modalities. ...

Person Tracking Using Audio-Video Sensor Fusion

Motivation: Audio and video signals originating from the same source tend to be related. To achieve optimal performance, a tracking system must exploit not just the statistics of each modality alone, but also relationships between the two. Consider a system that tracks moving objects. Such a system may use video data to track the spatial location of an object. If an object emits sound, such a s...

Analyzing and Evaluating Markerless Motion Tracking Using Inertial Sensors

In this paper, we introduce a novel framework for automatically evaluating the quality of 3D tracking results obtained from markerless motion capturing. In our approach, we use additional inertial sensors to generate suitable reference information. In contrast to previously used marker-based evaluation schemes, inertial sensors are inexpensive, easy to operate, and impose comparatively weak add...