A fast Human detection algorithm for container reach stackers

2018 ◽  
Vol 0 (0) ◽  
pp. 0-0
Author(s):  
Chao Mi ◽  
◽  
Mengtong Wu ◽  
Weijian Mi ◽  
Jun Wang ◽  
...  
Keyword(s):  
Detection Algorithm ◽  
Human Detection

Implementation of a Single Human Detection Algorithm for Video Digital Door Lock

2012 ◽  
Vol 19B (2) ◽  
pp. 127-134
Author(s):  
Seung-Hwan Shin ◽  
Sang-Rak Lee ◽  
Han-Go Choi
Keyword(s):  
Detection Algorithm ◽  
Human Detection ◽  
Door Lock

Fast Human Detection Algorithm Based on Subtraction Stereo for Generic Environment

2011 ◽  
Author(s):  
Alessandro Moro ◽  
Makoto Arie ◽  
Kenji Terabayashi ◽  
Kazunori Umeda ◽  
Tuan D. Pham ◽  
...  
Keyword(s):  
Detection Algorithm ◽  
Human Detection

Human Detection Algorithm Optimization in Machine Vision

2020 ◽  
Vol 57 (10) ◽  
pp. 101006
Author(s):  
何倩倩 He Qianqian ◽  
张荣芬 Zhang Rongfen ◽  
刘宇红 Liu Yuhong
Keyword(s):  
Machine Vision ◽  
Detection Algorithm ◽  
Human Detection ◽  
Algorithm Optimization

Human detection algorithm for seismic and ultrasonic detectors.

2008 ◽  
Vol 124 (4) ◽  
pp. 2499-2499
Author(s):  
Alexander E. Ekimov ◽  
James M. Sabatier
Keyword(s):  
Detection Algorithm ◽  
Human Detection

scholarly journals Two-Stream RGB-D Human Detection Algorithm Based on RFB Network

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 123175-123181
Author(s):  
Wenli Zhang ◽  
Jiaqi Wang ◽  
Xiang Guo ◽  
Kaizhen Chen ◽  
Ning Wang
Keyword(s):  
Detection Algorithm ◽  
Human Detection

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.

scholarly journals Validation of an Automatic Arousal Detection Algorithm for Whole-Night Sleep EEG Recordings

2020 ◽  
Vol 2 (3) ◽  
pp. 258-272
Author(s):  
Daphne Chylinski ◽  
Franziska Rudzik ◽  
Dorothée Coppieters ‘t Wallant ◽  
Martin Grignard ◽  
Nora Vandeleene ◽  
...  
Keyword(s):  
Visual Detection ◽  
Sleep Stage ◽  
Automatic Detection ◽  
Detection Algorithm ◽  
Human Detection ◽  
Sleep Eeg ◽  
Older Individuals ◽  
Time Frequency ◽  
Eeg Recordings ◽  
Research Centres

Arousals during sleep are transient accelerations of the EEG signal, considered to reflect sleep perturbations associated with poorer sleep quality. They are typically detected by visual inspection, which is time consuming, subjective, and prevents good comparability across scorers, studies and research centres. We developed a fully automatic algorithm which aims at detecting artefact and arousal events in whole-night EEG recordings, based on time-frequency analysis with adapted thresholds derived from individual data. We ran an automated detection of arousals over 35 sleep EEG recordings in healthy young and older individuals and compared it against human visual detection from two research centres with the aim to evaluate the algorithm performance. Comparison across human scorers revealed a high variability in the number of detected arousals, which was always lower than the number detected automatically. Despite indexing more events, automatic detection showed high agreement with human detection as reflected by its correlation with human raters and very good Cohen’s kappa values. Finally, the sex of participants and sleep stage did not influence performance, while age may impact automatic detection, depending on the human rater considered as gold standard. We propose our freely available algorithm as a reliable and time-sparing alternative to visual detection of arousals.

scholarly journals Vision based Office Automation and Security System using Machine Learning and Internet of Things

2018 ◽  
Vol 7 (2.24) ◽  
pp. 42
Author(s):  
Amber Goel ◽  
Apaar Khurana ◽  
Pranav Sehgal ◽  
K Suganthi
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Face Recognition ◽  
Internet Of Things ◽  
Office Automation ◽  
Detection Algorithm ◽  
Machine Learning Algorithms ◽  
Human Detection ◽  
Raspberry Pi ◽  
Efficient Tool

The paper focuses on two areas, automation and security. Raspberry Pi is the heart of the project and it is fuelled by Machine Learning Algorithms using Open CV and Internet of Things. Face recognition uses Linear Binary Pattern and if an unknown person uses their workstation, a message will be sent to the respective person with the photo of the person who uses the workstation. Face recognition is also being used for uploading attendance and switching ON and OFF appliances automatically. During un-official hours, A Human Detection algorithm is being used to detect the human presence. If an unknown person enters the office, a photo of the person will be taken and sent to the authorities. This technology is a combination of Computer Vision, Machine learning and Internet of things, that serves to be an efficient tool for both automation and security.  

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