scholarly journals Monocular Robust Depth Estimation Vision System for Robotic Tasks Interventions in Metallic Targets

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3220 ◽  
Author(s):  
Carlos Veiga Almagro ◽  
Mario Di Castro ◽  
Giacomo Lunghi ◽  
Raúl Marín Prades ◽  
Pedro José Sanz Valero ◽  
...  
Keyword(s):  
Object Tracking ◽  
Vision System ◽  
Nuclear Research ◽  
Depth Estimation ◽  
Work Load ◽  
Manual Control ◽  
Repetitive Tasks ◽  
Autonomous Mode ◽  
Robotic Tasks ◽  
Tracking And Recognition

Robotic interventions in hazardous scenarios need to pay special attention to safety, as in most cases it is necessary to have an expert operator in the loop. Moreover, the use of a multi-modal Human-Robot Interface allows the user to interact with the robot using manual control in critical steps, as well as semi-autonomous behaviours in more secure scenarios, by using, for example, object tracking and recognition techniques. This paper describes a novel vision system to track and estimate the depth of metallic targets for robotic interventions. The system has been designed for on-hand monocular cameras, focusing on solving lack of visibility and partial occlusions. This solution has been validated during real interventions at the Centre for Nuclear Research (CERN) accelerator facilities, achieving 95% success in autonomous mode and 100% in a supervised manner. The system increases the safety and efficiency of the robotic operations, reducing the cognitive fatigue of the operator during non-critical mission phases. The integration of such an assistance system is especially important when facing complex (or repetitive) tasks, in order to reduce the work load and accumulated stress of the operator, enhancing the performance and safety of the mission.

Combining Ultrasonic Signals And Multi-Colored Images To Perform Object Tracking And Recognition In Low-Cost Robotic Platforms

2016 ◽  
Author(s):  
Danilo H. F. Menezes ◽  
Thiago D. Mendonca ◽  
Wolney M. Neto ◽  
Hendrik T. Macedo ◽  
Leonardo N. Matos
Keyword(s):  
Object Tracking ◽  
Low Cost ◽  
Ultrasonic Signals ◽  
Robotic Platforms ◽  
Tracking And Recognition

scholarly journals Deep Learning-Based Object Detection, Localisation and Tracking for Smart Wheelchair Healthcare Mobility

2020 ◽  
Vol 18 (1) ◽  
pp. 91
Author(s):  
Louis Lecrosnier ◽  
Redouane Khemmar ◽  
Nicolas Ragot ◽  
Benoit Decoux ◽  
Romain Rossi ◽  
...  
Keyword(s):  
Object Detection ◽  
Object Tracking ◽  
Indoor Environment ◽  
Distance Estimation ◽  
Depth Estimation ◽  
Detection Algorithm ◽  
Use Case ◽  
Detection Distance ◽  
Detection Depth ◽  
Sort Algorithm

This paper deals with the development of an Advanced Driver Assistance System (ADAS) for a smart electric wheelchair in order to improve the autonomy of disabled people. Our use case, built from a formal clinical study, is based on the detection, depth estimation, localization and tracking of objects in wheelchair’s indoor environment, namely: door and door handles. The aim of this work is to provide a perception layer to the wheelchair, enabling this way the detection of these keypoints in its immediate surrounding, and constructing of a short lifespan semantic map. Firstly, we present an adaptation of the YOLOv3 object detection algorithm to our use case. Then, we present our depth estimation approach using an Intel RealSense camera. Finally, as a third and last step of our approach, we present our 3D object tracking approach based on the SORT algorithm. In order to validate all the developments, we have carried out different experiments in a controlled indoor environment. Detection, distance estimation and object tracking are experimented using our own dataset, which includes doors and door handles.

Object Tracking and Recognition Based on Reliability Assessment of Learning in Mobile Environments

2016 ◽  
Vol 94 (2) ◽  
pp. 267-282 ◽  
Author(s):  
Youngseop Kim ◽  
Woori Han ◽  
Yong-Hwan Lee ◽  
Cheong Ghil Kim ◽  
Kuinam J. Kim
Keyword(s):  
Object Tracking ◽  
Reliability Assessment ◽  
Mobile Environments ◽  
Assessment Of Learning ◽  
Tracking And Recognition

scholarly journals On Assessing Driver Awareness of Situational Criticalities: Multi-modal Bio-Sensing and Vision-Based Analysis, Evaluations, and Insights

2020 ◽  
Vol 10 (1) ◽  
pp. 46
Author(s):  
Siddharth Siddharth ◽  
Mohan M. Trivedi
Keyword(s):  
New Technologies ◽  
Vision System ◽  
Driver Assistance Systems ◽  
Sensing Systems ◽  
Autonomous Mode ◽  
Skin Response ◽  
User Data ◽  
Multiple Sensor ◽  
Short Time ◽  
Awareness Context

Automobiles for our roadways are increasingly using advanced driver assistance systems. The adoption of such new technologies requires us to develop novel perception systems not only for accurately understanding the situational context of these vehicles, but also to infer the driver’s awareness in differentiating between safe and critical situations. This manuscript focuses on the specific problem of inferring driver awareness in the context of attention analysis and hazardous incident activity. Even after the development of wearable and compact multi-modal bio-sensing systems in recent years, their application in driver awareness context has been scarcely explored. The capability of simultaneously recording different kinds of bio-sensing data in addition to traditionally employed computer vision systems provides exciting opportunities to explore the limitations of these sensor modalities. In this work, we explore the applications of three different bio-sensing modalities namely electroencephalogram (EEG), photoplethysmogram (PPG) and galvanic skin response (GSR) along with a camera-based vision system in driver awareness context. We assess the information from these sensors independently and together using both signal processing- and deep learning-based tools. We show that our methods outperform previously reported studies to classify driver attention and detecting hazardous/non-hazardous situations for short time scales of two seconds. We use EEG and vision data for high resolution temporal classification (two seconds) while additionally also employing PPG and GSR over longer time periods. We evaluate our methods by collecting user data on twelve subjects for two real-world driving datasets among which one is publicly available (KITTI dataset) while the other was collected by us (LISA dataset) with the vehicle being driven in an autonomous mode. This work presents an exhaustive evaluation of multiple sensor modalities on two different datasets for attention monitoring and hazardous events classification.

Active multispot projection for object tracking and recognition

2004 ◽  
Author(s):  
Wei Su ◽  
Laurence G. Hassebrook ◽  
Veera G. Yalla
Keyword(s):  
Object Tracking ◽  
Tracking And Recognition

Sliding Mode Controller for Stereo Vision Based Autonomous Flight of Quad-Rotor M

2011 ◽  
Vol 23 (1) ◽  
pp. 137-148 ◽  
Author(s):  
Dwi Pebrianti ◽  
◽  
WeiWang ◽  
Daisuke Iwakura ◽  
Yuze Song ◽  
...  
Keyword(s):  
Object Tracking ◽  
Stereo Vision ◽  
Flight Control ◽  
Background Subtraction ◽  
Sliding Mode ◽  
Vision System ◽  
Sliding Mode Controller ◽  
Model Errors ◽  
Stereo Vision System ◽  
Aerial Vehicle

We have investigated the possibility of a Sliding Mode Controller (SMC) for autonomous hovering and waypoint of a quad-rotor Micro Aerial Vehicle (MAV) based on an on ground stereo vision system. The object tracking used here is running average background subtraction. Among the background subtraction algorithms for object tracking, running average is known to have the fastest processing speed and the lowest memory requirement. Stereo vision system is known to have a good performance in measuring the distance from camera to object without any information regarding the object geometry in advance. SMC is known to have advantage of insensitivity to the model errors, parametric uncertainties and other disturbances. The experiment on autonomous hovering and way-point by using running average method for object tracking and SMC for the flight control shows a reliable result.

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