scholarly journals Super resolution passive radars based on 802.11ax Wi‐Fi signals for human movement detection

2021 ◽  
Author(s):  
Hasan Can Yildirim ◽  
Jean‐François Determe ◽  
Laurent Storrer ◽  
François Rottenberg ◽  
Philippe De Doncker ◽  
...  
Keyword(s):  
Super Resolution ◽  
Human Movement ◽  
Movement Detection

scholarly journals Real Life Human Movement Realization in Multimodal Group Communication Using Depth Map Information and Machine Learning

2020 ◽  
Vol 2 (2) ◽  
pp. 93-101
Author(s):  
Dr. Ranganathan G.
Keyword(s):  
Data Gathering ◽  
Real Life ◽  
Depth Map ◽  
Human Movement ◽  
Recognition System ◽  
Research Field ◽  
Movement Detection ◽  
The Real ◽  
Human Movements ◽  
Almost All

The latest advancements in the evolution of depth map information’s has paved way for interesting works like object recognition sign detection and human movement detection etc. The real life human movement detection or their activity identification is very challenging and tiresome. Since the real life activities of the humans could be of much interest in almost all areas, the subject of identifying the human activities has gained significance and has become a most popular research field. Identifying the human movements /activities in the public places like airport, railways stations, hospital, home for aged become very essential due to the several benefits incurred form the human movement recognition system such as surveillance camera, monitoring devices etc. since the changes in the space and the time parameters can provide an effective way of presenting the movements, yet in the case of natural color vision, as the flatness is depicted in almost all portions of images. So the work laid out in the paper in order to identify the human movement in the real life employs the space and the time depth particulars (Spatial-Temporal depth details –STDD) and the random forest in the final stage for movement classification. The technology put forth utilize the Kinect sensors to collecting the information’s in the data gathering stage. The mechanism laid out to identify the human movements is test with the MATLAB using the Berkley and the Cornell datasets. The mechanism proposed through the acquired results proves to deliver a better performance compared to the human movements captured using the normal video frames.

scholarly journals Human Movement Detection using Recurrent Convolutional Neural Networks

2019 ◽  
Vol 8 (12S) ◽  
pp. 348-351
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Human Movement ◽  
Sensor Data ◽  
Light Weight ◽  
Movement Detection ◽  
Relative Coordinates ◽  
Video Input ◽  
Background Image ◽  
Over Time

Human Movement detection is vital in Tele-presence Robots, Animations, Games and Robotic movements. By using Traditional methods with the help of sensor suits it is difficult to find and interpret the movements. As it includes so much sensor data which is difficult to interpret, find the action and send to long distances. It is also very expensive and bulky too. Image processing and computer vision provides a solution to detect and interpret Human movement based on R-CNN approach. It is cheap, easy and light weight algorithm. It takes the video input and divides it in to frames, then it is Human body is separated for the background image. This paper mainly focused on skeleton, its major points and its relative positions in successive picture frames. A set of frames (Video) is given as input to the model, so that the model compares the coordinates of the successive frames and estimates the movement. First, the human is identified and separated from the rest of the image by drawing a bounding box around the human by using CNN (Convolution neural networks), then by applying R-CNN human is segmented and converted to skeleton. From the shape of the skeleton we can identify whether the skeleton is that of a human or not. Comparing the relative coordinates of skeletons extracted from frames photographed over time gives the movement of the human and its direction.

scholarly journals Validity of using tri-axial accelerometers to measure human movement—Part I: Posture and movement detection

2014 ◽  
Vol 36 (2) ◽  
pp. 169-176 ◽  
Author(s):  
Vipul Lugade ◽  
Emma Fortune ◽  
Melissa Morrow ◽  
Kenton Kaufman
Keyword(s):  
Human Movement ◽  
Movement Detection

Hardware Implementation of a Human Movement Detection FMCW Radar

2021 ◽  
Author(s):  
Mohammad M. Abdul-Atty ◽  
Mohamed Mabrouk ◽  
Ahmed S.I Amar
Keyword(s):  
Hardware Implementation ◽  
Human Movement ◽  
Movement Detection ◽  
Fmcw Radar
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