Enhance Egocentric Grasp Recognition Based Flex Sensor Under Low Illumination

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.