High-Speed Human Detection Using a Multiresolution Cascade of Histograms of Oriented Gradients

2009 ◽  
pp. 48-55 ◽  
Author(s):  
Marco Pedersoli ◽  
Jordi Gonzàlez ◽  
Juan José Villanueva
Keyword(s):  
High Speed ◽  
Human Detection ◽  
Histograms Of Oriented Gradients

scholarly journals Human Detection by Boosting-Based Co-occurrence of HOG and Color Feature

1970 ◽  
Vol 5 (2) ◽  
pp. 47-51
Author(s):  
Ryo Matsumura ◽  
Akitoshi Hanazawa
Keyword(s):  
Human Detection ◽  
Color Feature ◽  
Histograms Of Oriented Gradients ◽  
Color Features ◽  
Local Areas

In this paper, we propose a method for human detection using co-occurrence of Histograms of Oriented Gradients (HOG) features and color features. This method expresses the co-occurrence between HOG and color features by Adaboost and generates the combination of the features effective for the identification automatically. Color features were calculated by making histograms that quantized hue and saturation in local areas. We show the effectiveness of the proposed method by identification experiments for human and non-human images.

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 Human Detection On Foggy Images

2019 ◽  
Vol 8 (6) ◽  
pp. 3637-3640
Keyword(s):  
Weather Condition ◽  
Weather Conditions ◽  
Human Detection ◽  
Least Square ◽  
Svm Classifier ◽  
Weighted Least Square ◽  
Histograms Of Oriented Gradients ◽  
Local Strength ◽  
Powerful Approach ◽  
Image Improvement

This paper was discussing about the human detection using SVM combining weighted least square-filter (WLS), histograms of oriented gradients (HOG). The combination of HOG and SVM is a powerful approach for human detection, as it uses local strength gradients; it is hard to handle noisy and foggy images. For removing of noise or fog from this type of images, we used weighted least square (WLS) filter, and then HOG and SVM algorithms are used for human detection. Due to deprived weather conditions such as fog and haze, the acquired images will exhibit damaged visibility. This can be occurred because of the presence of the suspended particles and scatter of light between objects and the camera. So the image improvement and renewal methods are used to improve the quality of an image which provide strong image in poor weather condition and can extract features from the images not only when they had illumination variations but also when they are degraded with fog. At last, detected objects can be categorized into predefined groups of humans and other objects by using SVM classifier

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