An effective real time gender recognition system for smart cameras

Maintenance professionals and other technical staff regularly need to learn to identify new parts in car engines and other equipment. The present work proposes a model of a task assistant based on a deep learning neural network. A YOLOv5 network is used for recognizing some of the constituent parts of an automobile. A dataset of car engine images was created and eight car parts were marked in the images. Then, the neural network was trained to detect each part. The results show that YOLOv5s is able to successfully detect the parts in real time video streams, with high accuracy, thus being useful as an aid to train professionals learning to deal with new equipment using augmented reality. The architecture of an object recognition system using augmented reality glasses is also designed.

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Design of Embedded Real-time Video Fire AI Recognition System

2021 International Conference on Computer Communication and Artificial Intelligence (CCAI) ◽

10.1109/ccai50917.2021.9447481 ◽

2021 ◽

Author(s):

Haofei He ◽

Pengjia Huang ◽

Aimin Xiong ◽

Jie Xiao ◽

Fangwu Li ◽

...

Keyword(s):

Real Time ◽

Recognition System

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DOLARS, a Distributed On-Line Activity Recognition System by Means of Heterogeneous Sensors in Real-Life Deployments—A Case Study in the Smart Lab of The University of Almería

Sensors ◽

10.3390/s21020405 ◽

2021 ◽

Vol 21 (2) ◽

pp. 405

Author(s):

Marcos Lupión ◽

Javier Medina-Quero ◽

Juan F. Sanjuan ◽

Pilar M. Ortigosa

Keyword(s):

Real Time ◽

Activity Recognition ◽

Real Life ◽

Recognition System ◽

Machine Learning Algorithms ◽

Sensor Data ◽

Heterogeneous Sensors ◽

On Line ◽

The University

Activity Recognition (AR) is an active research topic focused on detecting human actions and behaviours in smart environments. In this work, we present the on-line activity recognition platform DOLARS (Distributed On-line Activity Recognition System) where data from heterogeneous sensors are evaluated in real time, including binary, wearable and location sensors. Different descriptors and metrics from the heterogeneous sensor data are integrated in a common feature vector whose extraction is developed by a sliding window approach under real-time conditions. DOLARS provides a distributed architecture where: (i) stages for processing data in AR are deployed in distributed nodes, (ii) temporal cache modules compute metrics which aggregate sensor data for computing feature vectors in an efficient way; (iii) publish-subscribe models are integrated both to spread data from sensors and orchestrate the nodes (communication and replication) for computing AR and (iv) machine learning algorithms are used to classify and recognize the activities. A successful case study of daily activities recognition developed in the Smart Lab of The University of Almería (UAL) is presented in this paper. Results present an encouraging performance in recognition of sequences of activities and show the need for distributed architectures to achieve real time recognition.

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Dynamic Gesture Recognition System with Gesture Spotting Based on Self-Organizing Maps

Applied Sciences ◽

10.3390/app11041933 ◽

2021 ◽

Vol 11 (4) ◽

pp. 1933

Author(s):

Hiroomi Hikawa ◽

Yuta Ichikawa ◽

Hidetaka Ito ◽

Yutaka Maeda

Keyword(s):

Real Time ◽

Gesture Recognition ◽

Weight Vector ◽

Recognition System ◽

Self Organizing Map ◽

Self Organizing Maps ◽

Time Dynamic ◽

System Input ◽

Self Organizing ◽

Gesture Spotting

In this paper, a real-time dynamic hand gesture recognition system with gesture spotting function is proposed. In the proposed system, input video frames are converted to feature vectors, and they are used to form a posture sequence vector that represents the input gesture. Then, gesture identification and gesture spotting are carried out in the self-organizing map (SOM)-Hebb classifier. The gesture spotting function detects the end of the gesture by using the vector distance between the posture sequence vector and the winner neuron’s weight vector. The proposed gesture recognition method was tested by simulation and real-time gesture recognition experiment. Results revealed that the system could recognize nine types of gesture with an accuracy of 96.6%, and it successfully outputted the recognition result at the end of gesture using the spotting result.

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