Pinned Repositories
Deep-Learning-Based-Indoor-Human-Following-of-Mobile-Robot-Using-Color-Feature
Abstract: Human following is one of the fundamental functions in human–robot interaction for mobile robots. This paper shows a novel framework with state-machine control in which the robot tracks the target person in occlusion and illumination changes, as well as navigates with obstacle avoidance while following the target to the destination. People are detected and tracked using a deep learning algorithm, called Single Shot MultiBox Detector, and the target person is identified by extracting the color feature using the hue-saturation-value histogram. The robot follows the target safely to the destination using a simultaneous localization and mapping algorithm with the LIDAR sensor for obstacle avoidance. We performed intensive experiments on our human following approach in an indoor environment with multiple people and moderate illumination changes. Experimental results indicated that the robot followed the target well to the destination, showing the effectiveness and practicability of our proposed system in the given environment.
models
Models and examples built with TensorFlow
Online-Bagging-and-Boosting-master
Machine learning project implementing the online version of Bagging and boosting algorithms
Algabri's Repositories
Algabri/Deep-Learning-Based-Indoor-Human-Following-of-Mobile-Robot-Using-Color-Feature
Abstract: Human following is one of the fundamental functions in human–robot interaction for mobile robots. This paper shows a novel framework with state-machine control in which the robot tracks the target person in occlusion and illumination changes, as well as navigates with obstacle avoidance while following the target to the destination. People are detected and tracked using a deep learning algorithm, called Single Shot MultiBox Detector, and the target person is identified by extracting the color feature using the hue-saturation-value histogram. The robot follows the target safely to the destination using a simultaneous localization and mapping algorithm with the LIDAR sensor for obstacle avoidance. We performed intensive experiments on our human following approach in an indoor environment with multiple people and moderate illumination changes. Experimental results indicated that the robot followed the target well to the destination, showing the effectiveness and practicability of our proposed system in the given environment.
Algabri/models
Models and examples built with TensorFlow
Algabri/Online-Bagging-and-Boosting-master
Machine learning project implementing the online version of Bagging and boosting algorithms