ROS-Based Human Detection and Tracking from a Wireless Controlled Mobile Robot Using Kinect

Sankar, Sidagam; Tsai, Chi-Yi

doi:10.3390/asi2010005

Cited by 17 publications

(4 citation statements)

References 11 publications

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“…Here is important to make camera calibrations, in order to map image pixel coordinates to floor coordinates. [12] presented a real-time remote-control system for human detection and tracking. In the proposed system is used a Kinect RGB-D camera as a visual sensing device.…”

Section: Literature Reviewmentioning

confidence: 99%

Target Tracking Using Reinforcement Learning and Neural Networks

Koroveshi

Ktona

2021

EJENG

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Target tracking is a process that may find applications in different domains such as video surveillance, robot navigation and human computer interaction. In this work we have considered the problem of tracking a moving object in a multi agent environment. The environment is a rectangular space bounded by walls. The first agent is the target and it moves randomly in the space. The second agent should follow the target, keeping as close as possible without crashing with it. It uses sensors to detect the position of the target. The sensor readings give the distance and the angle from the target. We use reinforcement learning to train the tracker to detect any change in the movement of the target and stay within a certain range from it. Reinforcement learning is a form of machine learning in which the agent learns by interacting with the environment. By doing so, for each action taken, the agent receives a reward from the environment, which is used to determine positive or negative behaviour. The goal of the agent is to maximise the total reward received during the interaction. This form of machine learning has applications in different areas, such as: game solving with the most known game being AlphaGO; robotics, for design of hard-to engineer behaviours; traffic light control, personalized recommendations, etc. The sensor readings may have continuous values, making a very large state space. We approximate the value function using neural networks and use different reward functions for learning the best policy.

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Section: Literature Reviewmentioning

confidence: 99%

Target Tracking Using Reinforcement Learning and Neural Networks

Koroveshi

Ktona

2021

EJENG

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“…For example, it has been used in 3D reconstruction for object modeling [ 10 , 11 , 12 , 13 ] and indoor scenes [ 14 , 15 ] and mobile robots’ navigation and mapping [ 16 , 17 , 18 , 19 ]. The industrial applications include palletizing tasks [ 20 , 21 ], safety [ 22 , 23 , 24 ], teleoperation [ 25 , 26 ], human body detection and tracking [ 27 , 28 , 29 ], and gesture recognition tasks [ 30 , 31 , 32 ]. Healthcare applications involve gait analysis and elderly monitoring [ 33 , 34 , 35 , 36 ], the reconstruction of human body kinematics thanks to augmented and virtual reality software based on Kinect v2 [ 37 , 38 ].…”

Section: Introductionmentioning

confidence: 99%

Experimental Procedure for the Metrological Characterization of Time-of-Flight Cameras for Human Body 3D Measurements

Pasinetti

Nuzzi

Luchetti

et al. 2023

Sensors

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Time-of-flight cameras are widely adopted in a variety of indoor applications ranging from industrial object measurement to human activity recognition. However, the available products may differ in terms of the quality of the acquired point cloud, and the datasheet provided by the constructors may not be enough to guide researchers in the choice of the perfect device for their application. Hence, this work details the experimental procedure to assess time-of-flight cameras’ error sources that should be considered when designing an application involving time-of-flight technology, such as the bias correction and the temperature influence on the point cloud stability. This is the first step towards a standardization of the metrological characterization procedure that could ensure the robustness and comparability of the results among tests and different devices. The procedure was conducted on Kinect Azure, Basler Blaze 101, and Basler ToF 640 cameras. Moreover, we compared the devices in the task of 3D reconstruction following a procedure involving the measure of both an object and a human upper-body-shaped mannequin. The experiment highlighted that, despite the results of the previously conducted metrological characterization, some devices showed evident difficulties in reconstructing the target objects. Thus, we proved that performing a rigorous evaluation procedure similar to the one proposed in this paper is always necessary when choosing the right device.

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“…A combination of both LiDAR and camera has also been used to perform SLAM [10,11] and obstacle/object detection (such as people and vehicles) [12,13,14]. In [15], Sankar and Tsai designed a ROS based remotely controlled mobile robot with a mounted camera for providing security in a densely crowded environment through human tracking and detection. However, the system is not autonomous and needs constant human supervision.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Intruder Detection Using a ROS-Based Multi-Robot System Equipped with 2D-LiDAR Sensors

Islam,

Ahmed,

Nuruddin

et al. 2020

Preprint

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The application of autonomous mobile robots in robotic security platforms is becoming a promising field of innovation due to their adaptive capability of responding to potential disturbances perceived through a wide range of sensors. Researchers have proposed systems that either focus on utilizing a single mobile robot or a system of cooperative multiple robots. However, very few of the proposed works, particularly in the field of multi-robot systems, are completely dependent on LiDAR sensors for achieving various tasks. This is essential when other sensors on a robot fail to provide peak performance in particular conditions, such as a camera operating in the absence of light. This paper proposes a multirobot system that is developed using ROS (Robot Operating System) for intruder detection in a single-range-sensor-perrobot scenario with centralized processing of detections from all robots by our central bot MIDNet (Multiple Intruder Detection Network). This work is aimed at providing an autonomous multi-robot security solution for a warehouse in the absence of human personnel.

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ROS-Based Human Detection and Tracking from a Wireless Controlled Mobile Robot Using Kinect

Cited by 17 publications

References 11 publications

Target Tracking Using Reinforcement Learning and Neural Networks

Target Tracking Using Reinforcement Learning and Neural Networks

Experimental Procedure for the Metrological Characterization of Time-of-Flight Cameras for Human Body 3D Measurements

Autonomous Intruder Detection Using a ROS-Based Multi-Robot System Equipped with 2D-LiDAR Sensors

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