Binarization based implementation for real-time human detection

2013 ◽  
Author(s):  
Shuai Xie ◽  
Yibin Li ◽  
Zhiping Jia ◽  
Lei Ju
Keyword(s):  
Real Time ◽  
Human Detection

Dynamic Dual-Peak Network: A real-time human detection network in crowded scenes

2021 ◽  
pp. 103195
Author(s):  
Yefan Xie ◽  
Jiangbin Zheng ◽  
Xuan Hou ◽  
Yue Xi ◽  
Fengming Tian
Keyword(s):  
Real Time ◽  
Human Detection ◽  
Crowded Scenes

Multi-scale bidirectional local template patterns for real-time human detection

2013 ◽  
Author(s):  
Jiu Xu ◽  
Ning Jiang ◽  
Xinwei Xue ◽  
Heming Sun ◽  
Wenxin Yu ◽  
...  
Keyword(s):  
Real Time ◽  
Human Detection ◽  
Multi Scale

scholarly journals Real-Time Human Detection as an Edge Service Enabled by a Lightweight CNN

2018 ◽  
Author(s):  
Seyed Yahya Nikouei ◽  
Yu Chen ◽  
Sejun Song ◽  
Ronghua Xu ◽  
Baek-Young Choi ◽  
...  
Keyword(s):  
Real Time ◽  
Human Detection

Real-Time Human Detection Based on the Improved Local Binary Pattern

2014 ◽  
Vol 1044-1045 ◽  
pp. 1246-1250
Author(s):  
Dong Mei Wu ◽  
Xing Ma ◽  
Jing Wang ◽  
Hao Zhang
Keyword(s):  
Real Time ◽  
Local Binary Pattern ◽  
Human Detection ◽  
Computational Method ◽  
Detection Accuracy ◽  
Real Time System ◽  
Feature Vectors ◽  
Linear Classifier ◽  
Detection Window ◽  
Testing Speed

By analyzing the detection accuracy and the testing speed of the Local Binary Pattern. we propose an improved LBP algorithm and apply it in human detection. Through the signs of the comparisons among neighboring pixels, it will get the histogram of the detection window. Then we can encode the global contour by the distribution coefficient of the histogram. when the Linear classifier is used, we propose a fast computational method that does not need to explicitly generate feature vectors and not require feature vectors normalization. experiment shows that this method has higher efficiency and can’t reduce the accuracy, it achieves 19 fps speed and can be used in a real-time system.

Efficient discriminative multiresolution cascade for real-time human detection applications

2011 ◽  
Vol 32 (13) ◽  
pp. 1581-1587 ◽  
Author(s):  
Marco Pedersoli ◽  
Jordi Gonzàlez ◽  
Andrew D. Bagdanov ◽  
Xavier Roca
Keyword(s):  
Real Time ◽  
Human Detection

Robot Vision System for Human Detection and Action Recognition

2020 ◽  
Vol 24 (3) ◽  
pp. 346-356
Author(s):  
Satoshi Hoshino ◽  
◽  
Kyohei Niimura
Keyword(s):  
Optical Flow ◽  
Real Time ◽  
Action Recognition ◽  
Autonomous Navigation ◽  
Vision System ◽  
Robot Vision ◽  
Human Detection ◽  
Time Performance ◽  
Camera Sensors ◽  
Camera Sensor

Mobile robots equipped with camera sensors are required to perceive humans and their actions for safe autonomous navigation. For simultaneous human detection and action recognition, the real-time performance of the robot vision is an important issue. In this paper, we propose a robot vision system in which original images captured by a camera sensor are described by the optical flow. These images are then used as inputs for the human and action classifications. For the image inputs, two classifiers based on convolutional neural networks are developed. Moreover, we describe a novel detector (a local search window) for clipping partial images around the target human from the original image. Since the camera sensor moves together with the robot, the camera movement has an influence on the calculation of optical flow in the image, which we address by further modifying the optical flow for changes caused by the camera movement. Through the experiments, we show that the robot vision system can detect humans and recognize the action in real time. Furthermore, we show that a moving robot can achieve human detection and action recognition by modifying the optical flow.

scholarly journals Research on a Fast Human-Detection Algorithm for Unmanned Surveillance Area in Bulk Ports

2014 ◽  
Vol 2014 ◽  
pp. 1-17 ◽  
Author(s):  
Chao Mi ◽  
Xin He ◽  
Haiwei Liu ◽  
Youfang Huang ◽  
Weijian Mi
Keyword(s):  
Real Time ◽  
Computing Time ◽  
Training Sample ◽  
Detection Algorithm ◽  
Human Detection ◽  
Security Requirements ◽  
Support Vector ◽  
Svm Classifier ◽  
Histograms Of Oriented Gradients ◽  
Heavy Equipment

With the development of port automation, most operational fields utilizing heavy equipment have gradually become unmanned. It is therefore imperative to monitor these fields in an effective and real-time manner. In this paper, a fast human-detection algorithm is proposed based on image processing. To speed up the detection process, the optimized histograms of oriented gradients (HOG) algorithm that can avoid the large number of double calculations of the original HOG and ignore insignificant features is used to describe the contour of the human body in real time. Based on the HOG features, using a training sample set consisting of scene images of a bulk port, a support vector machine (SVM) classifier combined with the AdaBoost classifier is trained to detect human. Finally, the results of the human detection experiments on Tianjin Port show that the accuracy of the proposed optimized algorithm has roughly the same accuracy as a traditional algorithm, while the proposed algorithm only takes 1/7 the amount of time. The accuracy and computing time of the proposed fast human-detection algorithm were verified to meet the security requirements of unmanned port areas.

Sign in / Sign up

Export Citation Format

Share Document