Abstract
Egocentric recognition is exciting computer vision research by acquiring images and video from the first-person overview. However, an image becomes noisy and dark under low illumination conditions, making subsequent hand detection tasks difficult. Thus, image enhancement is necessary to make buried detail more visible. This article addresses the challenge of egocentric hand grasp recognition in low light conditions by utilizing the flex sensor and image enhancement algorithm based on adaptive gamma correction with weighting distribution. Initially, a flex sensor is installed to the thumb for object manipulation. The thumb placement that holds in a different position on the object of each grasp affects the voltage changing of the flex sensor circuit. The average voltages are used to configure the weighting parameter to improve images in the image enhancement stage. Moreover, the contrast and gamma function are used to adjust varies the low light condition. These grasp images are then separated to be training and testing with pre-trained deep neural networks as the feature extractor in YOLOv2 detection network for the grasp recognition system. The proposed of using a flex sensor significantly improves the grasp recognition rate in low light conditions.
Highlights
Hands are the priority for humans that allow us to collaborate with the matters and the surroundings, correspond with others and carry out daily activities like dining, cleaning and dressing
The flex sensor is attached to the thumb to provide information on the movement, and that is another parameter that enhances the ability to differentiate each hand grasp
This paper showed how to detect and recognize hands grasp in egocentric vision by combining a flex sensor with image enhancement and the YOLOv2 architecture
Summary
Hands are the priority for humans that allow us to collaborate with the matters and the surroundings, correspond with others and carry out daily activities like dining, cleaning and dressing Focused on their significance, the computer vision researchers have attempted to analyze hands from various aspects: determine the position of the hand in the image [1], analyze the hands from multiple perspectives: localizing them in the images are investigated in any types of actions [2–4], as well as interact with the computer and the robot [5–7]. The essential characteristic of egocentric vision is providing a first-person perspective of the scene by laying a forward-facing wearable camera on the chest or head. The proposed system recognize the grasp with Deep Convolutional Neural Networks (DCNN) as the feature extractor and detection that has emerged as a valuable tool for computer vision tasks
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
