A New Pedestrian Feature Description Method Named Neighborhood Descriptor of Oriented Gradients

2021 ◽  
Vol 16 (1) ◽  
pp. 23-55
Author(s):  
Qian Liu ◽  
Feng Yang ◽  
XiaoFen Tang
Keyword(s):  
Feature Vector ◽  
Pedestrian Detection ◽  
Weight Vector ◽  
Svm Classifier ◽  
Feature Descriptor ◽  
Local Weight ◽  
Detection Approach ◽  
Training Samples ◽  
New Feature ◽  
Linear Svm

In view of the issue of the mechanism for enhancing the neighbourhood relationship of blocks of HOG, this paper proposes neighborhood descriptor of oriented gradients (NDOG), an improved feature descriptor based on HOG, for pedestrian detection. To obtain the NDOG feature vector, the algorithm calculates the local weight vector of the HOG feature descriptor, while integrating spatial correlation among blocks, concatenates this weight vector to the tail of the HOG feature descriptor, and uses the gradient norm to normalize this new feature vector. With the proposed NDOG feature vector along with a linear SVM classifier, this paper develops a complete pedestrian detection approach. Experimental results for the INRIA, Caltech-USA, and ETH pedestrian datasets show that the approach achieves a lower miss rate and a higher average precision compared with HOG and other advanced methods for pedestrian detection especially in the case of insufficient training samples.

scholarly journals Cursor Movement Detection in Brain-Computer-Interface Systems Using the Hybrid K-Means Clustering Method and LSVM

2020 ◽  
Author(s):  
Leila Mohammadi ◽  
zahra einalou ◽  
Hamidreza Hosseinzadeh ◽  
Mehrdad Dadgostar
Keyword(s):  
Feature Extraction ◽  
Clustering Algorithm ◽  
Feature Vector ◽  
Support Vector ◽  
Svm Classifier ◽  
Clustering Method ◽  
Feature Extraction And Selection ◽  
New Feature ◽  
Linear Svm ◽  
The Right

Abstract In this study, we present the detection of the up- and downward as well as the right- and leftward motion of cursor based on feature extraction. Feature Extraction and selection for finding the proper classifier among the data mining methods are of great importance. In the proposed method, the hybrid K-means clustering algorithm and the linear support vector machine (LSVM) classifier have been used for extracting the important features and detecting the cursor motion. In this algorithm, the K-means clustering method is used to recognize the available hidden patterns in each of the four modes (up, down, left, and right). The identification of these patterns can raise the accuracy of classification. The membership degree of each feature vector in the proposed new patterns is considered as a new feature vector corresponding to the previous feature vector and then, the cursor motion is detected using the linear SVM classifier. The database of the Karadeniz Technical University of Turkey has been used in the present article. Applying the proposed method for data based on the hold-up cross validation causes the accuracy of the classifier in the up- and downward and left- and rightward movements in each person to increase by 2–10%.

scholarly journals Dihedral Group D4—A New Feature Extraction Algorithm

Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 548
Author(s):  
Puneet Sharma
Keyword(s):  
Dihedral Group ◽  
Feature Vector ◽  
Feature Descriptor ◽  
Image Region ◽  
Histograms Of Oriented Gradients ◽  
Wide Range ◽  
Extraction Algorithm ◽  
Complete Set ◽  
New Feature ◽  
Group D

In this paper, we propose a new feature descriptor for images that is based on the dihedral group D 4 , the symmetry group of the square. The group action of the D 4 elements on a square image region is used to create a vector space that forms the basis for the feature vector. For the evaluation, we employed the Error-Correcting Output Coding (ECOC) algorithm and tested our model with four diverse datasets. The results from the four databases used in this paper indicate that the feature vectors obtained from our proposed D 4 algorithm are comparable in performance to that of Histograms of Oriented Gradients (HOG) model. Furthermore, as the D 4 model encapsulates a complete set of orientations pertaining to the D 4 group, it enables its generalization to a wide range of image classification applications.

A Robust Pedestrian Detection Approach Based on Support Vector Machine Using Convex Hull

2014 ◽  
Vol 573 ◽  
pp. 501-507
Author(s):  
M.K. Revathi ◽  
P. Annapandi ◽  
K.P. Ramya
Keyword(s):  
Support Vector Machine ◽  
Convex Hull ◽  
Pedestrian Detection ◽  
Classification Performance ◽  
Point Of View ◽  
Support Vector ◽  
Svm Classifier ◽  
Theoretical Point ◽  
Detection Approach ◽  
Geometrical Features

The support vector machine (SVM), an assuring new method for the classification, has been widely used in many areas efficiently. However, the online learning issue of SVM is still not addressed satisfactorily since when a new sample arrives to retrain the SVM to adjust the classifier. This may not be feasible for real-time applications due to the expensive computation cost for re-training the SVM. This paper propose an Online SVM classifier algorithm known as OSVM-CH, which is based on the convex hull vertices selection depends on geometrical features of SVM. From the theoretical point of view, the first d+1(d is the dimension of the input samples) selected samples are proved to be vertices of the convex hull. This guarantees that the selected samples in our method keep the greatest amount of information of the convex hull. From the pedestrian detection application point of view, the new algorithm can update the classifier without reducing its classification performance.

scholarly journals Random Forest with Adaptive Local Template for Pedestrian Detection

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Tao Xiang ◽  
Tao Li ◽  
Mao Ye ◽  
Zijian Liu
Keyword(s):  
Computer Vision ◽  
Random Forest ◽  
Classification Accuracy ◽  
Template Matching ◽  
Detection Method ◽  
State Of The Art ◽  
Pedestrian Detection ◽  
Sliding Window ◽  
Experimental Results ◽  
Training Samples

Pedestrian detection with large intraclass variations is still a challenging task in computer vision. In this paper, we propose a novel pedestrian detection method based on Random Forest. Firstly, we generate a few local templates with different sizes and different locations in positive exemplars. Then, the Random Forest is built whose splitting functions are optimized by maximizing class purity of matching the local templates to the training samples, respectively. To improve the classification accuracy, we adopt a boosting-like algorithm to update the weights of the training samples in a layer-wise fashion. During detection, the trained Random Forest will vote the category when a sliding window is input. Our contributions are the splitting functions based on local template matching with adaptive size and location and iteratively weight updating method. We evaluate the proposed method on 2 well-known challenging datasets: TUD pedestrians and INRIA pedestrians. The experimental results demonstrate that our method achieves state-of-the-art or competitive performance.

A Novel Fast Pedestrian Detection Method

2015 ◽  
Vol 738-739 ◽  
pp. 538-541
Author(s):  
Fu Qiang Zhou ◽  
Yan Li
Keyword(s):  
Video Streaming ◽  
Local Binary Pattern ◽  
Detection Method ◽  
Pedestrian Detection ◽  
Original Method ◽  
Histograms Of Oriented Gradients ◽  
Detection Approach

This paper presents novel pedestrian detection approach in video streaming, which could process frames rapidly. The method is based on cascades of HOG-LBP (Histograms of Oriented Gradients-Local Binary Pattern), but combines non-negative factorization to reduce the length of the feature, aiming at realizing a more efficient way of detection, remedying the slowness of the original method. Experiments show our method can process faster than HOG and HOG-LBP, and more accurate than HOG, which has better performance in pedestrian detection in video streaming.

Pedestrian Detection Based on Histograms of Oriented Gradients in ROI

2012 ◽  
Vol 542-543 ◽  
pp. 937-940
Author(s):  
Ping Shu Ge ◽  
Guo Kai Xu ◽  
Xiu Chun Zhao ◽  
Peng Song ◽  
Lie Guo
Keyword(s):  
Traditional Method ◽  
Detection Method ◽  
Feature Vector ◽  
Pedestrian Detection ◽  
Region Of Interest ◽  
Experimental Results ◽  
Process Time ◽  
Vertical Edge ◽  
Histograms Of Oriented Gradients

To locate pedestrian faster and more accurately, a pedestrian detection method based on histograms of oriented gradients (HOG) in region of interest (ROI) is introduced. The features are extracted in the ROI where the pedestrian's legs may exist, which is helpful to decrease the dimension of feature vector and simplify the calculation. Then the vertical edge symmetry of pedestrian's legs is fused to confirm the detection. Experimental results indicate that this method can achieve an ideal accuracy with lower process time compared to traditional method.

scholarly journals Classification of agricultural fields using time series of dual polarimetry TerraSAR-X images

2014 ◽  
Vol XL-2/W3 ◽  
pp. 191-196 ◽  
Author(s):  
S. Mirzaee ◽  
M. Motagh ◽  
H. Arefi ◽  
M. Nooryazdan
Keyword(s):  
Environmental Changes ◽  
Support Vector ◽  
Svm Classifier ◽  
Sensing Applications ◽  
Imaging Characteristics ◽  
Training Samples ◽  
Study Results ◽  
Phenological Changes ◽  
Multi Temporal

Due to its special imaging characteristics, Synthetic Aperture Radar (SAR) has become an important source of information for a variety of remote sensing applications dealing with environmental changes. SAR images contain information about both phase and intensity in different polarization modes, making them sensitive to geometrical structure and physical properties of the targets such as dielectric and plant water content. In this study we investigate multi temporal changes occurring to different crop types due to phenological changes using high-resolution TerraSAR-X imagers. The dataset includes 17 dual-polarimetry TSX data acquired from June 2012 to August 2013 in Lorestan province, Iran. Several features are extracted from polarized data and classified using support vector machine (SVM) classifier. Training samples and different features employed in classification are also assessed in the study. Results show a satisfactory accuracy for classification which is about 0.91 in kappa coefficient.

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