Vision-based human action recognition (HAR) is a hot topic of research from the decade due to a few popular applications such as visual surveillance and robotics. For correct action recognition, various local and global points are requires known as features. These features modified during the variation in human movement. But due to a bit change in several human actions, the features of these actions are mixed that degrade the recognition performance. In this article, we design a new 26-layered Convolutional Neural Network (CNN) architecture for accurate complex action recognition. The features are extracted from the global average pooling layer and fully connected (FC) layer, and fused by a proposed high entropy-based approach. Further, we propose a feature selection method name Poisson distribution along with Univariate Measures (PDaUM). Few of fused CNN features are irrelevant, and few of them are redundant that makes the incorrect prediction among complex human actions. Therefore, the proposed PDaUM based approach selects only the strongest features that later passed to the Extreme Learning Machine (ELM) and Softmax for final recognition. Four datasets are using for experimental analysis - HMDB51 (51 classes), UCF Sports (10 classes), KTH (6 classes), and Weizmann (10 classes). On these datasets, the ELM classifier gives an improved performance as compared to a Softmax classifier. The achieved accuracy on each dataset is 81.4%, 99.2%, 98.3%, and 98.7%, respectively. Comparison with existing techniques, it is shown that the proposed architecture gives better performance in terms of accuracy and testing time.

A resource conscious human action recognition framework using 26-layered deep convolutional neural network

Human action recognition is used in many applications such as video surveillance, human–computer interaction, assistive living, and gaming. Many papers have appeared in the literature showing that the fusion of vision and inertial sensing improves recognition accuracies compared to the situations when each sensing modality is used individually. This article provides a survey of the papers in which vision and inertial sensing are used simultaneously within a fusion framework in order to perform human action recognition. The surveyed papers are categorized in terms of fusion approaches, features, classifiers, as well as multimodality datasets considered. Challenges as well as possible future directions are also stated for deploying the fusion of these two sensing modalities under realistic conditions.

Vision and Inertial Sensing Fusion for Human Action Recognition: A Review

Digital-enabled manufacturing systems require a high level of automation for fast and low-cost production but should also present flexibility and adaptiveness to varying and dynamic conditions in their environment, including the presence of human beings; however, this presence of workers in the shared workspace with robots decreases the productivity, as the robot is not aware about the human position and intention, which leads to concerns about human safety. This issue is addressed in this work by designing a reliable safety monitoring system for collaborative robots (cobots). The main idea here is to significantly enhance safety using a combination of recognition of human actions using visual perception and at the same time interpreting physical human–robot contact by tactile perception. Two datasets containing contact and vision data are collected by using different volunteers. The action recognition system classifies human actions using the skeleton representation of the latter when entering the shared workspace and the contact detection system distinguishes between intentional and incidental interactions if physical contact between human and cobot takes place. Two different deep learning networks are used for human action recognition and contact detection, which in combination, are expected to lead to the enhancement of human safety and an increase in the level of cobot perception about human intentions. The results show a promising path for future AI-driven solutions in safe and productive human–robot collaboration (HRC) in industrial automation.

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A Mixed-Perception Approach for Safe Human–Robot Collaboration in Industrial Automation

Machine vision, an interdisciplinary field that aims to replicate human visual perception in computers, has experienced rapid progress and significant contributions. This paper traces the origins of machine vision, from early image processing algorithms to its convergence with computer science, mathematics, and robotics, resulting in a distinct branch of artificial intelligence. The integration of machine learning techniques, particularly deep learning, has driven its growth and adoption in everyday devices. This study focuses on the objectives of computer vision systems: replicating human visual capabilities including recognition, comprehension, and interpretation. Notably, image classification, object detection, and image segmentation are crucial tasks requiring robust mathematical foundations. Despite the advancements, challenges persist, such as clarifying terminology related to artificial intelligence, machine learning, and deep learning. Precise definitions and interpretations are vital for establishing a solid research foundation. The evolution of machine vision reflects an ambitious journey to emulate human visual perception. Interdisciplinary collaboration and the integration of deep learning techniques have propelled remarkable advancements in emulating human behavior and perception. Through this research, the field of machine vision continues to shape the future of computer systems and artificial intelligence applications.

A Review of Machine Learning and Deep Learning for Object Detection, Semantic Segmentation, and Human Action Recognition in Machine and Robotic Vision

Action recognition based on RGB and skeleton data sets: A survey

Multiple stream deep learning model for human action recognition

Human action analysis based on deep learning has become a hotspot in the fields of intelligent video. Recently, the approaches in depth-based human action recognition provide other ways to recognize human actions. The depth provide important supplementary information to improve the performance based on RGB stream. Meanwhile, the deep learning methods are effective for both RGB and depth features representation. In this paper, we use the deep learning model to learn the discriminative patterns for human action recognition from the depth-based motion history images (MHIs). This method is evaluated on both the 3D human action datasets RGBD-HuDaAct and NTU RGB+D. The experimental results show that our proposed approach achieves good accuracy for recognizing the indoor actions.

Depth MHI Based Deep Learning Model for Human Action Recognition

The invention discloses a method and device for extracting a human head image based on deep learning. The method comprises the steps of inputting a to-be-extracted picture including a portrait into apre-trained YOLO neural network for processing and outputting a head region image, inputting the head region image into a trained HED neural network for processing and outputting a head edge contour image, inputting the head region image serving as a content image and the head edge contour image serving as a style image into an artistic style transfer neural network for processing, and thus outputting a synthetic image. The device comprises a memory and a processor. The head edge contour image can be extracted from the head region image, and the style of the head edge contour image is transferred to the head region image, thereby realizing the integration of the details such as the head edge contour, the five sense organs and the hairs, and enabling the synthetic image to have rich facialfeatures and clear edge contour. The method and device disclosed by the invention are applied to the technical field of image recognition and processing.

Method and device for extracting human head image based on deep learning

 Traffic speed prediction is a crucial task of the intelligent traffic system. However, due to the highly nonlinear temporal patterns and non-static spatial dependence of traffic data, timely and accurate traffic forecasting remains a challenge. The existing methods usually use a static adjacency matrix to model spatial dependence while ignoring the spatial dynamic characteristics of the road network.Meanwhile, the dynamic influence of different time steps on the prediction target is ignored. Thus, we propose a dynamic multi-graph convolution recurrent neural network (DMGCRNN), which models the dynamic correlations of road networks over time based on various information of road network. Dynamic correlation is an essential factor for accurate traffic prediction, because it reflects the change of the traffic conditions in real-time. In this model, we design a dynamic graph construction method, which utilizes the local temporal and spatial characteristics of each road segment to construct dynamic graphs. Then, a dynamic multi-graph convolution fusion module is proposed, which considers the dynamic characteristics of spatial correlations and global information to model the dynamic trend of spatial dependence. Moreover, by combing the global context information, temporal attention is provided to capture the dynamic temporal dependence among different time steps. The experimental results from two real-world traffic datasets demonstrate that our method outperforms the state-of-the-art baselines.

Dynamic multi-graph convolution recurrent neural network for traffic speed prediction

This paper presents a head detection and extraction method that can be used in robotic portrait drawing. First, using the state-of-the-art, real-time object detection system-YOLO(You Only Look Once), we train the model to automatically detect human heads directly from the image. Then we utilize the holistically-nested edge detection (HED) algorithm to extract head edges by performing image-to-image prediction. Finally, the content image of the head can be synthesized into a head edge map with a style synthesis algorithm. The synthesized image can be sent to the robot for drawing. Our method was verified and evaluated on the drawing robot we developed.

Deep learning-based human head detection and extraction for robotic portrait drawing

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