Human Detection Method Based on Non-Redundant Gradient Semantic Local Binary Patterns

2015 ◽  
Vol E98.A (8) ◽  
pp. 1735-1742 ◽  
Author(s):  
Jiu XU ◽  
Ning JIANG ◽  
Wenxin YU ◽  
Heming SUN ◽  
Satoshi GOTO
Keyword(s):  
Detection Method ◽  
Local Binary Patterns ◽  
Human Detection

A New Moving Human Detection Method in Color Video Image

2012 ◽  
Vol 229-231 ◽  
pp. 1166-1170
Author(s):  
Tia Nai Wu ◽  
Yun Rong Wu ◽  
Yun Yu Wu
Keyword(s):  
Background Subtraction ◽  
Detection Method ◽  
Main Idea ◽  
Color Difference ◽  
Human Detection ◽  
Background Model ◽  
Video Frame ◽  
Difference Model ◽  
Background Updating ◽  
Background Image

Moving object detection is the basic of video applications such as computer vision, object recognition and tracking, surveillance security etc. Background subtraction and symmetrical differencing are the popular methods of motion detection. The main idea of them is to compare the current video frame with a specified background image or a background model or the next video frame. For background subtraction, the obtaining of initialization is crucial and many methods have been employed, so it is necessary to model background to adapt the changes of background. In this paper, the single gaussian modeling as the initialization background model combined with an improved linear alternate background updating method is proposed. And then, a novel moving human detection method which employs background subtraction and symmetrical differencing based on rgb color difference model is presented. The experimental results show that the detection method can detect moving human effectively and real-time.

Human Detection Based on a New Gradient CENTRIST Feature

2014 ◽  
Vol 543-547 ◽  
pp. 2716-2719
Author(s):  
Tao Li ◽  
Tao Xiang ◽  
Ren Jie Huang ◽  
Xue Zhu Zhao
Keyword(s):  
Real Time ◽  
Detection Method ◽  
Feature Vector ◽  
Detection Performance ◽  
Human Detection ◽  
Feature Descriptor ◽  
Image Patch ◽  
Method Base

This paper proposes a real-time and accurate human detection method base on a new Gradient CENTRIST feature descriptor. Firstly, the feature can characterizes not only local human appearance and shape but also implicitly represent the global contour. Secondly, it does not involve image pre-processing or feature vector normalization, and it only requires steps to test an image patch. Our main contribution is that a more reliable feature descriptor is found, which can get a better human detection. The experiments on the INRIA pedestrian dataset demonstrate that the detection performance is significantly improved.

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