Improved Deep Neural Network Object Tracking System for Applications in Home Robotics

Erol, Berat A.; Majumdar, Abhijit; Lwowski, Jonathan; Benavidez, Patrick; Rad, Paul; Jamshidi, Mo

doi:10.1007/978-3-319-89629-8_14

Cited by 39 publications

(16 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To evaluate the effectiveness of a model, specific test scenarios are needed, and different models require different methods [33][34][35][36][37][38][39][40][41][42][43]. To test the effectiveness of the designed model, the program was coded in Code Vision at the mechatronics lab of Rayan Shid Nama Eng.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Prevention of Metro Rail Accidents and Incidents in Stations Using RFID Technology

Sahba

2018

2018 World Automation Congress (WAC)

View full text Add to dashboard Cite

Today, metro is one of the urban infrastructure and plays an important role in urban transport. The safety and health of people in a city are always important, and transport in the metro should also be safe. When subway trains operate, it is possible to occur various accidents such as an exit from the rails or collision with a possible obstacle on the rails (human or another train). In this paper, a model is proposed based on RFID technology in which the train is equipped with a RFID reader, and a control circuit with a microcontroller as well as placing RFID active tags at specific points of the path. When the train approaches the tagged points of the route, the train tag reader scans the tag number, and then the microcontroller identifies the status of the environment by retrieving the information from the internal database. Then, the control circuit adjusts the rotational speed of the electric motor and the train speed consequently based on that data. In this way, the speed of the train is adapted automatically and promptly to the conditions and the probability of an accident in unforeseen circumstances is reduced.

show abstract

Section: Experiments and Resultsmentioning

confidence: 99%

Prevention of Metro Rail Accidents and Incidents in Stations Using RFID Technology

Sahba

2018

2018 World Automation Congress (WAC)

View full text Add to dashboard Cite

show abstract

“…A home-based human-robot interaction (HRI) system object-tracking mechanism for non-GPS environments using an improved deep neural network object tracking system for applications in home robotics [49]. A multi-agent system for gamification between householders and also between families managing smart homes [50].…”

Section: Topic and Objective Technology And Feature Outcome And Futurmentioning

confidence: 99%

“…The second group of the AISH dataset includes Jiang [74], Sodagari [75], Lynggaard [76], Liu, et al [77], Erol, Majumdar, Lwowski, Benavidez, Rad and Jamshidi [49], Jiang, et al [78]. As shown previously, these papers have utilized IoT and AI algorithms (see Figure 8), where they have also referred to energy efficiency in different contexts and power saving, network energy consumption, and network utility cost.…”

Section: Group 2: Power and Energymentioning

confidence: 99%

“…Erol, Majumdar, Lwowski, Benavidez, Rad and Jamshidi [49] presented a new independent home deep neural network object-tracking mechanism for non-GPS environments using pattern recognition and machine learning techniques for home-based human-robot interaction (HRI) system. The system consists of an assistive robot (Kobuki Turtlebot 2, modified with ASUS Xtion Pro Live RGB-D camera, powered with ODROID XU4 microcontroller, and BOSCH BND055 IMU installed camera), Amazon Echo device, Amazon service for Alexa, Amazon web services, Alexa custom skill software, machine learning techniques, and AI methods (different algorithms like deep convolutional neural networks (CNN)).…”

Section: Artificial Intelligence and Indoor Systemsmentioning

confidence: 99%

See 1 more Smart Citation

A Systematic Content Review of Artificial Intelligence and the Internet of Things Applications in Smart Home

et al. 2020

View full text Add to dashboard Cite

This article reviewed the state-of-the-art applications of the Internet of things (IoT) technology applied in homes for making them smart, automated, and digitalized in many respects. The literature presented various applications, systems, or methods and reported the results of using IoT, artificial intelligence (AI), and geographic information system (GIS) at homes. Because the technology has been advancing and users are experiencing IoT boom for smart built environment applications, especially smart homes and smart energy systems, it is necessary to identify the gaps, relation between current methods, and provide a coherent instruction of the whole process of designing smart homes. This article reviewed relevant papers within databases, such as Scopus, including journal papers published in between 2010 and 2019. These papers were then analyzed in terms of bibliography and content to identify more related systems, practices, and contributors. A designed systematic review method was used to identify and select the relevant papers, which were then reviewed for their content by means of coding. The presented systematic critical review focuses on systems developed and technologies used for smart homes. The main question is ”What has been learned from a decade trailing smart system developments in different fields?”. We found that there is a considerable gap in the integration of AI and IoT and the use of geospatial data in smart home development. It was also found that there is a large gap in the literature in terms of limited integrated systems for energy efficiency and aged care system development. This article would enable researchers and professionals to fully understand those gaps in IoT-based environments and suggest ways to fill the gaps while designing smart homes where users have a higher level of thermal comfort while saving energy and greenhouse gas emissions. This article also raised new challenging questions on how IoT and existing developed systems could be improved and be further developed to address other issues of energy saving, which can steer the research direction to full smart systems. This would significantly help to design fully automated assistive systems to improve quality of life and decrease energy consumption.

show abstract

“…The objective of visual tracking is to track an arbitrary object in a video, where the ground-truth bounding box is given in the first frame [1][2][3]. It is one of the essential computer vision tasks for many application areas such as video surveillance, autonomous navigation, and self driving [4][5][6]. The visual object tracking problem is challenging because the appearance of the target object is given in the first frame only.…”

Section: Introductionmentioning

confidence: 99%

Fully Convolutional Single-Crop Siamese Networks for Real-Time Visual Object Tracking

Lee

2019

Electronics

View full text Add to dashboard Cite

The visual object tracking problem seeks to track an arbitrary object in a video, and many deep convolutional neural network-based algorithms have achieved significant performance improvements in recent years. However, most of them do not guarantee real-time operation due to the large computation overhead for deep feature extraction. This paper presents a single-crop visual object tracking algorithm based on a fully convolutional Siamese network (SiamFC). The proposed algorithm significantly reduces the computation burden by extracting multiple scale feature maps from a single image crop. Experimental results show that the proposed algorithm demonstrates superior speed performance in comparison with that of SiamFC.

show abstract

Improved Deep Neural Network Object Tracking System for Applications in Home Robotics

Cited by 39 publications

References 29 publications

Prevention of Metro Rail Accidents and Incidents in Stations Using RFID Technology

Prevention of Metro Rail Accidents and Incidents in Stations Using RFID Technology

A Systematic Content Review of Artificial Intelligence and the Internet of Things Applications in Smart Home

Fully Convolutional Single-Crop Siamese Networks for Real-Time Visual Object Tracking

Contact Info

Product

Resources

About