Detecting good quality frames in videos captured by a wearable camera for blind navigation
Long Tian, Yingli Tian, Chucai Yi · 2013
Recent technology developments in computer vision, digital cameras, and portable computers make it possible to assist blind individuals by developing camera-based object recognition products. However, motion blur caused by a moving camera limits the real-world application of wayfinding for blind users. In this paper, we propose a new method to detect good quality frames from videos captured by cameras, which are taken by blind users. In our proposed method, both gradient and intensity statistics are extracted from video frames. Then a support vector machine (SVM) based classifier is applied to identify the frames with good quality (Unblurred) from those blurred frames. The Unblurred frames will be further processed to extract essential information for blind wayfinding and navigation such as signage recognition and text extraction. Experimental results demonstrate that our proposed method is able to robustly handle video motions in both indoor and outdoor environments.