Abstract
Laser pointers are one of the most widely used interactive and pointing devices in different human-computer interaction systems. Existing approaches to vision-based laser spot tracking are designed for controlled indoor environments with the main assumption that the laser spot is very bright, if not the brightest, spot in images. In this work, we are interested in developing a method for an outdoor, open-space environment, which could be implemented on embedded devices with limited computational resources. Under these circumstances, none of the assumptions of existing methods for laser spot tracking can be applied, yet a novel and fast method with robust performance is required. Throughout the paper, we will propose and evaluate an efficient method based on modified circular Hough transform and Lucas–Kanade motion analysis. Encouraging results on a representative dataset demonstrate the potential of our method in an uncontrolled outdoor environment, while achieving maximal accuracy indoors. Our dataset and ground truth data are made publicly available for further development.
Highlights
A laser spot is used as input information in a number of laser pointer—camera sensing systems, such as interactive interfaces [1,2,3,4], laser guided robots [5,6,7], assistive technology application [8] and range measurements [9]
Each column represents the percentage of correct detections of order n in all input images, where order n = k means that the actual position of the laser spot is included in the candidate list in the first k candidates
When the laser spot is at a distance of up to 10 m from the camera, the accuracy of the first detection decreases to 79%
Summary
A laser spot is used as input information in a number of laser pointer—camera sensing systems, such as interactive interfaces [1,2,3,4], laser guided robots [5,6,7], assistive technology application [8] and range measurements [9]. Different methods have been proposed for detection and tracking of a laser spot. In a domotic control system [8], template matching combined with fuzzy rules is proposed for the detection of a laser spot. The reported success rate is 69% with tuned fuzzy rule parameters on the test set of 105 images (the size of the overall image set is 210). In [10], a laser spot tracking algorithm based on a subdivision mesh is proposed. In [1], a laser spot detection-based computer interface is presented, where the method is based on perceptron learning. Insufficient testing data, as declared by the authors, leaves the applicability of the method for real-world applications inconclusive.
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