scholarly journals Machine Vision Enabled Bot for Object Tracking

2020 ◽  
Vol 1 (1) ◽  
pp. 15-26
Author(s):  
Rupali Patil ◽  
Adhish Velingkar ◽  
Mohammad Nomaan Parmar ◽  
Shubham Khandhar ◽  
Bhavin Prajapati
Keyword(s):  
Machine Learning ◽  
Object Tracking ◽  
Cloud Storage ◽  
Object Motion ◽  
Human Machine Interaction ◽  
Camera Control ◽  
Live Feed ◽  
Detection And Tracking ◽  
Left And Right ◽  
Machine Interaction

Object detection and tracking are essential and testing undertaking in numerous PC vision appliances. To distinguish the object first find a way to accumulate information. In this design, the robot can distinguish the item and track it just as it can turn left and right position and afterward push ahead and in reverse contingent on the object motion. It keeps up the consistent separation between the item and the robot. We have designed a webpage that is used to display a live feed from the camera and the camera can be controlled by the user efficiently. Implementation of machine learning is done for detection purposes along with open cv and creating cloud storage. The pan-tilt mechanism is used for camera control which is attached to our 3-wheel chassis robot through servo motors. This idea can be used for surveillance purposes, monitoring local stuff, and human-machine interaction.

scholarly journals IOT based Advanced Medicine Dispenser Integrated with an Interactive Web Application

2018 ◽  
Vol 7 (4.10) ◽  
pp. 46
Author(s):  
Nanda Kishor Panda ◽  
Shubham Bhardwaj ◽  
H. Bharadwaj ◽  
Rohil Singhvi
Keyword(s):  
Machine Learning ◽  
Web Application ◽  
The Internet ◽  
Human Machine Interaction ◽  
Everyday Objects ◽  
Front End ◽  
Healthcare Monitoring ◽  
User Friendly ◽  
Machine Interaction ◽  
Interactive User

Internet of Things (IOT) is a development of the internet which plays a  major role in integrating human-machine interaction by allowing everyday objects to send and receive data in a variety of applications. Using IOT in healthcare monitoring provides an avenue for doctors and patients to interact and to track the dosage of medication administered. The paper presents an interactive, user friendly network integrated with an automated medicine dispenser which uses IOT, cloud computing and machine learning. The network was built on a python tornado framework with a front end developed using materialise CSS. The feasibility of this approach was validated by building a prototype and conducting a survey.  

Simultaneous 3D Motion Detection, Long-Term Tracking and Model Reconstruction for Multi-Objects

2019 ◽  
Vol 16 (04) ◽  
pp. 1950017
Author(s):  
Sheng Liu ◽  
Yangqing Wang ◽  
Fengji Dai ◽  
Jingxiang Yu
Keyword(s):  
3D Reconstruction ◽  
Object Tracking ◽  
Motion Detection ◽  
Visual Information ◽  
Detection Accuracy ◽  
Human Machine Interaction ◽  
Reconstruction Process ◽  
3D Motion ◽  
Machine Interaction

Motion detection and object tracking play important roles in unsupervised human–machine interaction systems. Nevertheless, the human–machine interaction would become invalid when the system fails to detect the scene objects correctly due to occlusion and limited field of view. Thus, robust long-term tracking of scene objects is vital. In this paper, we present a 3D motion detection and long-term tracking system with simultaneous 3D reconstruction of dynamic objects. In order to achieve the high precision motion detection, an optimization framework with a novel motion pose estimation energy function is provided in the proposed method by which the 3D motion pose of each object can be estimated independently. We also develop an accurate object-tracking method which combines 2D visual information and depth. We incorporate a novel boundary-optimization segmentation based on 2D visual information and depth to improve the robustness of tracking significantly. Besides, we also introduce a new fusion and updating strategy in the 3D reconstruction process. This strategy brings higher robustness to 3D motion detection. Experiments results show that, for synthetic sequences, the root-mean-square error (RMSE) of our system is much smaller than Co-Fusion (CF); our system performs extremely well in 3D motion detection accuracy. In the case of occlusion or out-of-view on real scene data, CF will suffer the loss of tracking or object-label changing, by contrast, our system can always keep the robust tracking and maintain the correct labels for each dynamic object. Therefore, our system is robust to occlusion and out-of-view application scenarios.

OBJECT MOTION DETECTION AND TRACKING BY AN ARTIFICIAL INTELLIGENCE APPROACH

2008 ◽  
Vol 22 (05) ◽  
pp. 915-928 ◽  
Author(s):  
LUCIA MADDALENA ◽  
ALFREDO PETROSINO ◽  
ALESSIO FERONE
Keyword(s):  
Artificial Intelligence ◽  
Object Detection ◽  
Object Tracking ◽  
Moving Object Detection ◽  
Multiple Hypothesis Testing ◽  
Object Motion ◽  
Moving Object ◽  
Surveillance Systems ◽  
Robust Detection ◽  
Detection And Tracking

The aim of this paper is to propose an artificial intelligence based approach to moving object detection and tracking. Specifically, we adopt an approach to moving object detection based on self organization through artificial neural networks. Such approach allows to handle scenes containing moving backgrounds and gradual illumination variations, and achieves robust detection for different types of videos taken with stationary cameras. Moreover, for object tracking we propose a suitable conjunction between Kalman filtering, properly instanced for the problem at hand, and a matching model belonging to the class of Multiple Hypothesis Testing. To assess the validity of our approach, we experimented both proposed moving object detection and object tracking over different color video sequences that represent typical situations critical for video surveillance systems.

scholarly journals Human-aided artificial intelligence: Or, how to run large computations in human brains? Toward a media sociology of machine learning

2019 ◽  
Vol 22 (10) ◽  
pp. 1868-1884 ◽  
Author(s):  
Rainer Mühlhoff
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cognitive Abilities ◽  
Human Machine Interaction ◽  
Technological Transformation ◽  
Media Sociology ◽  
Media Technologies ◽  
Media Philosophy ◽  
Human Brains ◽  
Machine Interaction

Today, artificial intelligence (AI), especially machine learning, is structurally dependent on human participation. Technologies such as deep learning (DL) leverage networked media infrastructures and human-machine interaction designs to harness users to provide training and verification data. The emergence of DL is therefore based on a fundamental socio-technological transformation of the relationship between humans and machines. Rather than simulating human intelligence, DL-based AIs capture human cognitive abilities, so they are hybrid human-machine apparatuses. From a perspective of media philosophy and social-theoretical critique, I differentiate five types of “media technologies of capture” in AI apparatuses and analyze them as forms of power relations between humans and machines. Finally, I argue that the current hype about AI implies a relational and distributed understanding of (human/artificial) intelligence, which I categorize under the term “cybernetic AI.” This form of AI manifests in socio-technological apparatuses that involve new modes of subjectivation, social control, and digital labor.

scholarly journals Self-Powered Intelligent Human-Machine Interaction for Handwriting Recognition

Research ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Hang Guo ◽  
Ji Wan ◽  
Haobin Wang ◽  
Hanxiang Wu ◽  
Chen Xu ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Human Computer Interaction ◽  
Supervised Machine Learning ◽  
Triboelectric Nanogenerator ◽  
Human Machine Interaction ◽  
The Neural Network ◽  
Interaction Interface ◽  
Machine Interaction ◽  
Computer Interaction

Handwritten signatures widely exist in our daily lives. The main challenge of signal recognition on handwriting is in the development of approaches to obtain information effectively. External mechanical signals can be easily detected by triboelectric nanogenerators which can provide immediate opportunities for building new types of active sensors capable of recording handwritten signals. In this work, we report an intelligent human-machine interaction interface based on a triboelectric nanogenerator. Using the horizontal-vertical symmetrical electrode array, the handwritten triboelectric signal can be recorded without external energy supply. Combined with supervised machine learning methods, it can successfully recognize handwritten English letters, Chinese characters, and Arabic numerals. The principal component analysis algorithm preprocesses the triboelectric signal data to reduce the complexity of the neural network in the machine learning process. Further, it can realize the anticounterfeiting recognition of writing habits by controlling the samples input to the neural network. The results show that the intelligent human-computer interaction interface has broad application prospects in signature security and human-computer interaction.

scholarly journals Prediction of state anxiety by machine learning applied to photoplethysmography data

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e10448
Author(s):  
David Perpetuini ◽  
Antonio Maria Chiarelli ◽  
Daniela Cardone ◽  
Chiara Filippini ◽  
Sergio Rinella ◽  
...  
Keyword(s):  
Machine Learning ◽  
State Anxiety ◽  
Affective Computing ◽  
Pulse Wave ◽  
Learning Algorithm ◽  
Nervous Activity ◽  
Human Machine Interaction ◽  
Promising Tool ◽  
Custom Made ◽  
Machine Interaction

Background As the human behavior is influenced by both cognition and emotion, affective computing plays a central role in human-machine interaction. Algorithms for emotions recognition are usually based on behavioral analysis or on physiological measurements (e.g., heart rate, blood pressure). Among these physiological signals, pulse wave propagation in the circulatory tree can be assessed through photoplethysmography (PPG), a non-invasive optical technique. Since pulse wave characteristics are influenced by the cardiovascular status, which is affected by the autonomic nervous activity and hence by the psychophysiological state, PPG might encode information about emotional conditions. The capability of a multivariate data-driven approach to estimate state anxiety (SA) of healthy participants from PPG features acquired on the brachial and radial artery was investigated. Methods The machine learning method was based on General Linear Model and supervised learning. PPG was measured employing a custom-made system and SA of the participants was assessed through the State-Trait Anxiety Inventory (STAI-Y) test. Results A leave-one-out cross-validation framework showed a good correlation between STAI-Y score and the SA predicted by the machine learning algorithm (r = 0.81; p = 1.87∙10−9). The preliminary results suggested that PPG can be a promising tool for emotions recognition, convenient for human-machine interaction applications.

How Artificial Intelligence, Machine Learning and Deep Learning are Radically Different?

2018 ◽  
Vol 8 (2) ◽  
pp. 1
Author(s):  
Tanya Tiwari ◽  
Tanuj Tiwari ◽  
Sanjay Tiwari
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Analysis Software ◽  
Science Data ◽  
Human Machine Interaction ◽  
Learning Techniques ◽  
Nested Hierarchy ◽  
Further Development ◽  
Machine Interaction

There is a lot of confusion these days about Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL). A computer system able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. Artificial Intelligence has made it possible. Deep learning is a subset of machine learning, and machine learning is a subset of AI, which is an umbrella term for any computer program that does something smart. In other words, all machine learning is AI, but not all AI is machine learning, and so forth. Machine Learning represents a key evolution in the fields of computer science, data analysis, software engineering, and artificial intelligence. Machine learning (ML)is a vibrant field of research, with a range of exciting areas for further development across different methods and applications. These areas include algorithmic interpretability, robustness, privacy, fairness, inference of causality, human-machine interaction, and security. The goal of ML is never to make “perfect” guesses, because ML deals in domains where there is no such thing. The goal is to make guesses that are good enough to be useful. Deep learning is a particular kind of machine learning that achieves great power and flexibility by learning to represent the world as nested hierarchy of concepts, with each concept defined in relation to simpler concepts, and more abstract representations computed in terms of less abstract ones. This paper gives an overview of artificial intelligence, machine learning & deep learning techniques and compare these techniques.

scholarly journals Inside-out Vision for Treatment Identification in Dental Setup using Machine Learning

2021 ◽  
Author(s):  
Shaheena Noor ◽  
Humera Noor Minhas ◽  
Muhammad Imran Saleem ◽  
Vali Uddin ◽  
Najma Ismat
Keyword(s):  
Machine Learning ◽  
Dental Treatment ◽  
Multinomial Logistic Regression ◽  
Human Machine Interaction ◽  
Machine Learning Applications ◽  
In The Wild ◽  
Recognition Of Objects ◽  
Dental Applications ◽  
Machine Interaction ◽  
Inside Out

Abstract Smart clinics have gained much popularity due to the technological advancements in areas like computer vision. The recognition of objects and activities and overall perceiving the environment lies at the core of such systems. This is essential not just for the eco-independent systems, but also for Human-Machine-Interaction - specially in scenarios with small work-areas like dental treatment. In this paper, we compare a number of machine learning mod- els (including Multinomial Logistic Regression, Lazy Instance-based Learning (IBk), Sequential Minimal Optimization (SMO), Hoe ding Tree and Random Tree) for robustly identifying dental treatments. We take the objects-focussed as input, which covers parameters like material, symptoms of the patient teeth and tools used by the dentist. We take advantage of the fact that the issue of identifying a particular treatment can be solved by recognizing the objects seen during an activity. We collected a dental dataset in-the-wild and ran our tests to find that integrating different parameters improves accuracy relative to using each one separately. However, we also noted that in certain cases using the symptoms stand-alone gave better results. Also, with respect to RMS error convergence, symptoms showed to have lower error compared to combined. Finally, we noticed that the combined approach led to longer build and test times for the machine learning models. This shows that in machine learning applications in general and in medical/dental applications in particular, adding more parameters does not always lead to improved results. Rather it depends on the ML tool used, the parameters considered and the data given as input.

Multiple vehicles detection and tracking for intelligent transport systems using machine learning approaches

2019 ◽  
Vol 70 (3) ◽  
pp. 214-224
Author(s):  
Bui Ngoc Dung ◽  
Manh Dzung Lai ◽  
Tran Vu Hieu ◽  
Nguyen Binh T. H.
Keyword(s):  
Machine Learning ◽  
Gaussian Mixture ◽  
Research Field ◽  
Transport Systems ◽  
Learning Approaches ◽  
Subtraction Method ◽  
Intelligent Transport Systems ◽  
Intelligent Transport ◽  
Detection And Tracking ◽  
Multiple Vehicles

Video surveillance is emerging research field of intelligent transport systems. This paper presents some techniques which use machine learning and computer vision in vehicles detection and tracking. Firstly the machine learning approaches using Haar-like features and Ada-Boost algorithm for vehicle detection are presented. Secondly approaches to detect vehicles using the background subtraction method based on Gaussian Mixture Model and to track vehicles using optical flow and multiple Kalman filters were given. The method takes advantages of distinguish and tracking multiple vehicles individually. The experimental results demonstrate high accurately of the method.

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