A scene perception system for visually impaired based on object detection and classification using multi-modal DCNN

05/22/2018
by   Baljit Kaur, et al.
0

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.

READ FULL TEXT

page 4

page 14

page 22

page 25

research
08/25/2022

Bridging the View Disparity of Radar and Camera Features for Multi-modal Fusion 3D Object Detection

Environmental perception with multi-modal fusion of radar and camera is ...
research
07/30/2023

Uncertainty-Encoded Multi-Modal Fusion for Robust Object Detection in Autonomous Driving

Multi-modal fusion has shown initial promising results for object detect...
research
03/18/2022

Improve few-shot voice cloning using multi-modal learning

Recently, few-shot voice cloning has achieved a significant improvement....
research
06/09/2017

Multi-Modal Obstacle Detection in Unstructured Environments with Conditional Random Fields

Reliable obstacle detection and classification in rough and unstructured...
research
07/06/2018

Multi-modal Non-line-of-sight Passive Imaging

We consider the non-line-of-sight (NLOS) imaging of an object using ligh...
research
07/09/2021

Multimodal Icon Annotation For Mobile Applications

Annotating user interfaces (UIs) that involves localization and classifi...
research
03/17/2023

GOOD: General Optimization-based Fusion for 3D Object Detection via LiDAR-Camera Object Candidates

3D object detection serves as the core basis of the perception tasks in ...

Please sign up or login with your details

Forgot password? Click here to reset