An edge detection framework conjoining with IMU data for assisting indoor navigation of visually impaired persons. (January 2017)

• Record Type: Journal Article
• Authors: Chan, Kit Yan; Engelke, Ulrich; Abhayasinghe, Nimsiri
• Is Part Of: Expert systems with applications. Volume 67(2017)
• Page Start: 272
• Page End: 284
• Publication Date: 2017-01
• Subjects: Edge detection -- Sigmoid function -- Particle swarm optimization -- Smartphone navigation -- Vision impaired persons -- Inertial measurement unit (IMU); Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2016.09.007
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: A framework with IMU is proposed to aid object detections for moving camera. It overcomes existing approaches that cannot detect contaminated image edges. It overcomes deblurring algorithms that are not suitable for real‐time implementation. Its topological structure is adapted continuously for changing indoor environment. Results show it is a strong candidate for reliable real‐time navigation. Abstract: Smartphone applications based on object detection techniques have recently been proposed to assist visually impaired persons with navigating indoor environments. In the smartphone, digital cameras are installed to detect objects which are important for navigation. Prior to detect the interested objects from images, edges on the objects have to be identified. Object edges are difficult to be detected accurately as the image is contaminated by strong image blur which is caused by camera movement. Although deblurring algorithms can be used to filter blur noise, they are computationally expensive and not suitable for real-time implementation. Also edge detection algorithms are mostly developed for stationary images without serious blur. In this paper, a modified sigmoid function (MSF) framework based on inertial measurement unit (IMU) is proposed to mitigate these problems. The IMU estimates blur levels to adapt the MSF which is computationally simple. When the camera is moving, the topological structure of the MSF is estimated continuously in order to improve effectiveness of edge detections. The performance of the MSF framework is evaluated by detecting object edges on video sequences associated with IMU data. The MSF framework is benchmarked against existing edge detection techniques and results show that it can obtain comparably lower errors. It is further shown that the computation time is significantly decreased compared to using techniques that deploy deblurring algorithms, thus making our proposed technique a strong candidate for reliable real-time navigation.