Kristoffer Öfjäll

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The Visual Object Tracking challenge 2014, VOT2014, aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance. Results of 38 trackers are 2 Authors Suppressed Due to Excessive Length presented. The number of tested trackers makes VOT 2014 the largest benchmark on short-term tracking to date. For(More)
While autonomously driving systems accumulate more and more sensors as well as highly specialized visual features and engineered solutions, the human visual system provides evidence that visual input and simple low level image features are sufficient for successful driving. In this paper we propose extensions (non-linear update and coherence weighting) to(More)
There are three major issues for visual object trackers: model representation, search and model update. In this paper we address the last two issues for a specific model representation, grid based distribution models by means of channel-based distribution fields. Particularly we address the comparison part of searching. Previous work in the area has used(More)
This paper presents an autonomous robotic system that incorporates novel Computer Vision, Machine Learning and Data Mining algorithms in order to learn to navigate and discover important visual entities. This is achieved within a Learning from Demonstration (LfD) framework, where policies are derived from example state-to-action mappings. For autonomous(More)
An online method for rapidly learning the inverse kinematics of a redundant robotic arm is presented addressing the special requirements of active vision for visual inspection tasks. The system is initialized with a model covering a small area around the starting position, which is then incrementally extended by exploration. The number of motions during(More)
Visual feature descriptors are essential elements in most computer and robot vision systems. They typically lead to an abstraction of the input data, images, or video, for further processing, such as clustering and machine learning. In clustering applications, the cluster center represents the prototypical descriptor of the cluster and estimates the(More)
The labyrinth game is a simple yet challenging platform, not only for humans but also for control algorithms and systems. The game is easy to understand but still very hard to master. From a system point of view, the ball behaviour is in general easy to model but close to the obstacles there are severe non-linearities. Additionally, the far from flat(More)
Recent years have shown great progress in driving assistance systems, approaching autonomous driving step by step. Many approaches rely on lane markers however, which limits the system to larger paved roads and poses problems during winter. In this work we explore an alternative approach to visual road following based on online learning. The system learns(More)
In tele-operated robotics applications, the primary information channel from the robot to its human operator is a video stream. For autonomous robotic systems however, a much larger selection of sensors is employed, although the most relevant information for the operation of the robot is still available in a single video stream. The issue lies in(More)
We propose a method for detecting obstacles on the railway in front of a moving train using a monocular thermal camera. The problem is motivated by the large number of collisions between trains and various obstacles, resulting in reduced safety and high costs. The proposed method includes a novel way of detecting the rails in the imagery, as well as a way(More)