A scene perception system for visually impaired based on object detection and classification using multi-modal DCNN
This addresses a practical problem for visually impaired people, but it appears incremental as it builds on existing object detection methods with multi-modal fusion.
The paper tackles scene perception for visually impaired individuals by developing a cost-effective system that detects and classifies objects in outdoor traffic scenes while providing distance information via voice output, achieving unspecified performance metrics.
This paper represents a cost-effective scene perception system aimed towards visually impaired individual. We use an odroid system integrated with an USB camera and USB laser that can be attached on the chest. The system classifies the detected objects along with its distance from the user and provides a voice output. Experimental results provided in this paper use outdoor traffic scenes. The object detection and classification framework exploits a multi-modal fusion based faster RCNN using motion, sharpening and blurring filters for efficient feature representation.