Computer Vision Based Indoor Navigation: A Visual Markers Evaluation

A Novel Marker Detection System for People with Visual Impairment Using the Improved Tiny-YOLOv3 Model

BACKGROUND AND OBJECTIVE

Daily activities such as shopping and navigating indoors are challenging problems for people with visual impairment. Researchers tried to find different solutions to help people with visual impairment navigate indoors and outdoors.

METHODS

We applied deep learning to help visually impaired people navigate indoors using markers. We propose a system to help them detect markers and navigate indoors using an improved Tiny-YOLOv3 model. A dataset was created by collecting marker images from recorded videos and augmenting them using image processing techniques such as rotation transformation, brightness, and blur processing. After training and validating this model, the performance was tested on a testing dataset and on real videos.

RESULTS

The contributions of this paper are: (1) We developed a navigation system to help people with visual impairment navigate indoors using markers; (2) We implemented and tested a deep learning model to detect Aruco markers in different challenging situations using Tiny-YOLOv3; (3) We implemented and compared several modified versions of the original model to improve detection accuracy. The modified Tiny-YOLOv3 model achieved an accuracy of 99.31% in challenging conditions and the original model achieved an accuracy of 96.11 %.

CONCLUSION

The training and testing results show that the improved Tiny-YOLOv3 models are superior to the original model.

An RSSI-Based Localization, Path Planning and Computer Vision-Based Decision Making Robotic System

A robotic navigation system operates flawlessly under an adequate GPS signal range, whereas indoor navigation systems use the simultaneous localization and mapping system or other vision-based localization systems. The sensor used in indoor navigation systems is not suitable for low power and small scale robotic systems. The wireless area network transmitting devices have fixed transmission power, and the receivers get the different values of signal strength based on their surrounding environments. In the proposed method, the received signal strength index (RSSI) values of three fixed transmitter units are measured every 1.6 m in mesh format and analyzed by the classifiers, and robot position can be mapped in the indoor area. After navigation, the robot analyzes objects and detects and recognize human faces with the help of object recognition and facial recognition-based classification methods respectively. This robot detects the intruder with the current position in an indoor environment.

Identification of Markers in Challenging Conditions for People with Visual Impairment Using Convolutional Neural Network

People with visual impairment face a lot of difficulties in their daily activities. Several researches have been conducted to find smart solutions using mobile devices to help people with visual impairment perform tasks. This paper focuses on using assistive technology to help people with visual impairment in indoor navigation using markers. The essential steps of a typical navigation system are identifying the current location, finding the shortest path to the destination, and navigating safely to the destination using navigation feedback. In this research, the authors proposed a system to help people with visual impairment in indoor navigation using markers. In this system, the authors have re-defined the identification step to a classification problem and used convolutional neural networks to identify markers. The main contributions of this paper are: (1) A system to help people with visual impairment in indoor navigation using markers. (2) Comparing QR codes with Aruco markers to prove that Aruco markers work better. (3) Convolutional neural network has been implemented and simplified to detect the candidate markers in challenging conditions and improve response time. (4) Comparing the proposed model with another model to prove that it gives better accuracy for training and testing.