Real-time detection of human body in videos

2012 ◽  
Author(s):  
Ondrej Smirg ◽  
Zdenek Smekal
Keyword(s):  
Real Time ◽  
Human Body ◽  
Real Time Detection

An Improvement Measure to Human Body Detection Based on Kinect Using Integral Images

2012 ◽  
Vol 263-266 ◽  
pp. 2619-2622
Author(s):  
Jianpin Han ◽  
Bo Ting Geng ◽  
Tian Tan
Keyword(s):  
Real Time ◽  
Human Body ◽  
Intelligent System ◽  
Detection Performance ◽  
Crucial Issue ◽  
The Real ◽  
Improvement Measure ◽  
Detection Window ◽  
Integral Images ◽  
Real Time Detection

Human body detection of intelligent system is an important and crucial issue, and this work has been studied for many years. Luciano Spinello achieved the real-time detection performance of human body based on Kinect with the help of GPU to accelerate to computation of features .But when its algorithm is realized in CPU, it can’t still achieve the real-time detection performance. This paper put forword a improvement measure to accelerate the computation of features. Features can be rapidly calculated by integral images, which was proposed by Qiang Zhu to detect object rapidly in 2001, abandoning the previous procession of using three line interpolation and Gauss filter, the improved algorithm, in CPU 3.10Ghz, RAM 2.85GB, 640*480 detection window, can achieve the average detection rate of 40 FPS. Performance gets promotion greatly.

scholarly journals Real-time Concealed Object Detection from Passive Millimeter Wave Images Based on the YOLOv3 Algorithm

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1678 ◽  
Author(s):  
Lei Pang ◽  
Hui Liu ◽  
Yang Chen ◽  
Jungang Miao
Keyword(s):  
Real Time ◽  
Millimeter Wave ◽  
Human Body ◽  
Small Sample ◽  
Detection Accuracy ◽  
Single Shot ◽  
Sample Data ◽  
Detection Of Objects ◽  
Real Time Detection ◽  
Detection Speed

The detection of objects concealed under people’s clothing is a very challenging task, which has crucial applications for security. When testing the human body for metal contraband, the concealed targets are usually small in size and are required to be detected within a few seconds. Focusing on weapon detection, this paper proposes using a real-time detection method for detecting concealed metallic weapons on the human body applied to passive millimeter wave (PMMW) imagery based on the You Only Look Once (YOLO) algorithm, YOLOv3, and a small sample dataset. The experimental results from YOLOv3-13, YOLOv3-53, and Single Shot MultiBox Detector (SSD) algorithm, SSD-VGG16, are compared ultimately, using the same PMMW dataset. For the perspective of detection accuracy, detection speed, and computation resource, it shows that the YOLOv3-53 model had a detection speed of 36 frames per second (FPS) and a mean average precision (mAP) of 95% on a GPU-1080Ti computer, more effective and feasible for the real-time detection of weapon contraband on human body for PMMW images, even with small sample data.

Real-Time Detection of Drowsiness Related Lane Departures Using Steering Wheel Angle

2012 ◽  
Author(s):  
Anthony D. McDonald ◽  
Chris Schwarz ◽  
John D. Lee ◽  
Timothy L. Brown
Keyword(s):  
Real Time ◽  
Steering Wheel ◽  
Real Time Detection

Real-time Detection of Aortic Valve in Echocardiography using Convolutional Neural Networks

2020 ◽  
Vol 16 (5) ◽  
pp. 584-591 ◽  
Author(s):  
Muhammad Hanif Ahmad Nizar ◽  
Chow Khuen Chan ◽  
Azira Khalil ◽  
Ahmad Khairuddin Mohamed Yusof ◽  
Khin Wee Lai
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Heart Disease ◽  
Aortic Valve ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Valvular Heart Disease ◽  
Detection System ◽  
Processing Unit ◽  
Real Time Detection

Background: Valvular heart disease is a serious disease leading to mortality and increasing medical care cost. The aortic valve is the most common valve affected by this disease. Doctors rely on echocardiogram for diagnosing and evaluating valvular heart disease. However, the images from echocardiogram are poor in comparison to Computerized Tomography and Magnetic Resonance Imaging scan. This study proposes the development of Convolutional Neural Networks (CNN) that can function optimally during a live echocardiographic examination for detection of the aortic valve. An automated detection system in an echocardiogram will improve the accuracy of medical diagnosis and can provide further medical analysis from the resulting detection. Methods: Two detection architectures, Single Shot Multibox Detector (SSD) and Faster Regional based Convolutional Neural Network (R-CNN) with various feature extractors were trained on echocardiography images from 33 patients. Thereafter, the models were tested on 10 echocardiography videos. Results: Faster R-CNN Inception v2 had shown the highest accuracy (98.6%) followed closely by SSD Mobilenet v2. In terms of speed, SSD Mobilenet v2 resulted in a loss of 46.81% in framesper- second (fps) during real-time detection but managed to perform better than the other neural network models. Additionally, SSD Mobilenet v2 used the least amount of Graphic Processing Unit (GPU) but the Central Processing Unit (CPU) usage was relatively similar throughout all models. Conclusion: Our findings provide a foundation for implementing a convolutional detection system to echocardiography for medical purposes.

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