Efficient Scale Invariant Human Detection Using Histogram of Oriented Gradients for IoT Services

2017 ◽  
Author(s):  
D. Sangeetha ◽  
P. Deepa
Keyword(s):  
Human Detection ◽  
Histogram Of Oriented Gradients ◽  
Scale Invariant

scholarly journals Small Human Group Detection and Validation using Pyramidal Histogram of Oriented Gradients and Gray Level Run Length Method

2019 ◽  
Vol 9 (2) ◽  
pp. 2387-2394
Keyword(s):  
Moving Objects ◽  
Human Detection ◽  
Gray Level ◽  
Histogram Of Oriented Gradients ◽  
Human Group ◽  
Run Length ◽  
Human Interface ◽  
Classification Feature ◽  
Group Detection ◽  
Human Pose

Over the decade’s human detection in security and surveillance system became dynamic research part in computer vision. This concern is focused by wide functions in several areas such as smart surveillance, multiple human interface, human pose characterization, person counting and person identification etc. Video surveillance organism mainly deals with recognition plus classification of moving objects with respect to several actions like walking, talking and hand shaking etc. The specific processing stages of small human group detection and validation includes frame generation, segmentation using hierarchical clustering, To achieve accurate classification feature descriptors namely Multi-Scale Completed Local Binary Pattern (MS-CLBP) and Pyramidal Histogram Of Oriented Gradients (PHOG) are employed to extract the features efficiently, Recurrent Neural Network (RNN) classifier helps to classify the features into human and group in a crowd, To extract statistical features Gray Level Run Length Method (GLRLM) is incorporated which helps in group validation.

Human Detection Based on SVM and Improved Histogram of Oriented Gradients

2013 ◽  
Vol 380-384 ◽  
pp. 3862-3865 ◽  
Author(s):  
Li Hong Zhang
Keyword(s):  
Support Vector Machine ◽  
Linear Function ◽  
Detection System ◽  
Features Extraction ◽  
The Body ◽  
Human Detection ◽  
Support Vector ◽  
Detection Accuracy ◽  
Histogram Of Oriented Gradients ◽  
Feature Description

Considering the fact that original histogram of oriented gradients (HOG) cannot extract the body local features in large image regions, its features are improved when extracted, then more gradient information are extracted and feature description operators can be obtained which describe human detail features better in lager image regions or detection windows. Considering speed, we select support vector machine (SVM) using linear function kernel as a classifier. Combining with HOG extraction and SVM training, the process includes three steps: features extraction, training and detection. Experiments show that while maintaining a relatively satisfactory speed the human detection system improves detection accuracy.

scholarly journals EVALUATING SECTOR RING HISTOGRAM OF ORIENTED GRADIENTS FILTER IN LOCATING HUMANS WITHIN UAV IMAGES

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 23-27
Author(s):  
M. Ghasemi ◽  
M. Varshosaz ◽  
S. Pirasteh
Keyword(s):  
Natural Disasters ◽  
Good Accuracy ◽  
Detection Algorithm ◽  
Human Detection ◽  
Histogram Of Oriented Gradients ◽  
Uav Images

Abstract. Developing systems to find injured people quickly after natural disasters is an important topic. In recent years, special attention has been paid to the use of UAV images for this purpose. In this regard, an accurate and strong feature is required. It is shown that the Sector Ring Histogram of Oriented Gradients, is a feature very much independent from rotation and scale. The aim of this paper is to evaluate the performance of a human detection algorithm which is based on this strong feature. Experiments carried out suggest that using SRHOG feature humans can be detected with an accuracy of 73.69%. However, despite giving good accuracy, SRHOG results contain more than 33.33 % false labels.

scholarly journals Histogram of Oriented Gradients with Cell Average Brightness for Human Detection

2016 ◽  
Vol 23 (1) ◽  
pp. 27-36 ◽  
Author(s):  
Marek Wójcikowski
Keyword(s):  
Hardware Acceleration ◽  
Human Detection ◽  
Support Vector ◽  
Detection Accuracy ◽  
Histogram Of Oriented Gradients ◽  
Average Cell ◽  
Average Brightness ◽  
Hardware Implementations ◽  
Proposed Modification ◽  
Cell Average

Abstract A modification of the descriptor in a human detector using Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM) is presented. The proposed modification requires inserting the values of average cell brightness resulting in the increase of the descriptor length from 3780 to 3908 values, but it is easy to compute and instantly gives ≈ 25% improvement of the miss rate at 10‒4 False Positives Per Window (FPPW). The modification has been tested on two versions of HOG-based descriptors: the classic Dalal-Triggs and the modified one, where, instead of spatial Gaussian masks for blocks, an additional central cell has been used. The proposed modification is suitable for hardware implementations of HOG-based detectors, enabling an increase of the detection accuracy or resignation from the use of some hardware-unfriendly operations, such as a spatial Gaussian mask. The results of testing its influence on the brightness changes of test images are also presented. The descriptor may be used in sensor networks equipped with hardware acceleration of image processing to detect humans in the images.

scholarly journals Improved Pedestrian Detection Based on Extended Histogram of Oriented Gradients

2013 ◽  
Vol 347-350 ◽  
pp. 3815-3820
Author(s):  
Li Hong Zhang ◽  
Lin Li
Keyword(s):  
Detection System ◽  
Pedestrian Detection ◽  
Human Detection ◽  
Support Vector ◽  
Detection Accuracy ◽  
Histogram Of Oriented Gradients ◽  
Multi Scale ◽  
Suppression Method ◽  
Overlap Ratio ◽  
Scale Detection

In order to further improve pedestrian detection accuracy and avoid the disadvantage of original histogram of oriented gradients (HOG), differential template, overlap ratio and normalization method and so on are improved when HOG features are extracted, then more gradient information are extracted and feature description operators can be obtained which describe human detail features better in lager image regions or detection windows. Considering speed, we select support vector machine (SVM) using linear function kernel as a classifier. Multi-scale detection technique and non maxima suppression method are employed for precisely locating the pedestrians in the image. Experiments show that the human detection system improves detection accuracy and still maintains a relatively satisfactory speed.

Sign in / Sign up

Export Citation Format

Share Document