Research of Human Body Detection and Tracking Algorithm

2013 ◽  
Vol 791-793 ◽  
pp. 1023-1027
Author(s):  
Gang Zhang ◽  
Bin Ouyang ◽  
Lu Ming Yu ◽  
Lei Zhang
Keyword(s):  
Human Body ◽  
Moving Objects ◽  
Tracking Algorithm ◽  
Support Vector ◽  
Svm Classifier ◽  
Histogram Of Oriented Gradient ◽  
Detection And Tracking ◽  
Multi Media ◽  
Teaching Video ◽  
Tracking Technology

In this paper, the proposed algorithm regards the human body object character symbol using Support Vector Machine (SVM) classifier to train and classify Histogram of Oriented Gradient (HOG) features, which improve the accuracy of human body detection. We use optical flow tracking algorithm based on corner points of the contour for tracking. Kalman filter is regarded as the predictor to predict the size and location of the searching object. Also, the size and location of track window is real-time updated. In this paper, we present an object tracking algorithm for multi-media teaching video shoot. Target tracking technology is used for the video image processing analysis. By extracting moving object, we can get information in the subsequent frames to determine the trajectory and size of moving objects. After analysis of a large number of experiments, we can draw the conclusion that the algorithm is effective.

Text Localisation for Roman Words from Shop Signage

2017 ◽  
Vol 14 (2) ◽  
pp. 49
Author(s):  
Nurbaity Sabri ◽  
Noor Hazira Yusof ◽  
Zaidah` Ibrahim ◽  
Zolidah Kasiran ◽  
Nur Nabilah Abu Mangshor
Keyword(s):  
Comparative Study ◽  
Kernel Function ◽  
Texture Features ◽  
Text Recognition ◽  
Primary Data ◽  
Support Vector ◽  
Svm Classifier ◽  
Histogram Of Oriented Gradient ◽  
Speeded Up Robust Features ◽  
Better Than

Text localisation determines the location of the text in an image. This process is performed prior to text recognition. Localising text on shop signage is a challenging task since the images of the shop signage consist of complex background, and the text occurs in various font types, sizes, and colours. Two popular texture features that have been applied to localise text in scene images are a histogram of oriented gradient (HOG) and speeded up robust features (SURF). A comparative study is conducted in this paper to determine which is better with support vector machine (SVM) classifier. The performance of SVM is influenced by its kernel function and another comparative study is conducted to identify the best kernel function. The experiments have been conducted using primary data collected by the authors. Results indicate that HOG with quadratic kernel function localises text for shop signage better than SURF.

Research on athlete training behavior based on improved support vector algorithm and target image detection

2020 ◽  
Vol 39 (4) ◽  
pp. 5725-5736
Author(s):  
Jiang Min
Keyword(s):  
Target Detection ◽  
Training Model ◽  
Detection Algorithm ◽  
Support Vector ◽  
Svm Classifier ◽  
Target Image ◽  
Classification Result ◽  
Detection And Tracking ◽  
Neural Network Algorithm ◽  
Fully Connected

In view of the defects and shortcomings of the traditional target detection and tracking algorithm in accurately detecting targets and targets in different scenarios, based on the current research status and technical level of target detection and tracking at home and abroad, this paper proposes a target detection algorithm and tracking method using neural network algorithm, and applies it to the athlete training model. Based on the Alex-Net network structure, this paper designs a three-layer convolutional layer and two layers of fully connected layers. The last layer is used as the input of the SVM classifier, and the target classification result is obtained by the SVM classifier. In addition, this article adds SPP-Layer between the convolutional layer and the fully connected layer, enabling the same dimension of the Feature Map to be obtained before the fully connected layer for different sized input images. The research results show that the proposed method has certain recognition effect and can be applied to athlete training.

scholarly journals Moving Object Classification Using 3D Point Cloud in Urban Traffic Environment

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
MingFang Zhang ◽  
Rui Fu ◽  
YingShi Guo ◽  
Li Wang
Keyword(s):  
Point Cloud ◽  
Moving Objects ◽  
Object Classification ◽  
Urban Traffic ◽  
Moving Object ◽  
Support Vector ◽  
Svm Classifier ◽  
3D Point Cloud ◽  
Cloud Data ◽  
Traffic Environment

Moving object classification is essential for autonomous vehicle to complete high-level tasks like scene understanding and motion planning. In this paper, we propose a novel approach for classifying moving objects into four classes of interest using 3D point cloud in urban traffic environment. Unlike most existing work on object recognition which involves dense point cloud, our approach combines extensive feature extraction with the multiframe classification optimization to solve the classification task when partial occlusion occurs. First, the point cloud of moving object is segmented by a data preprocessing procedure. Then, the efficient features are selected via Gini index criterion applied to the extended feature set. Next, Bayes Decision Theory (BDT) is employed to incorporate the preliminary results from posterior probability Support Vector Machine (SVM) classifier at consecutive frames. The point cloud data acquired from our own LIDAR as well as public KITTI dataset is used to validate the proposed moving object classification method in the experiments. The results show that the proposed SVM-BDT classifier based on 18 selected features can effectively recognize the moving objects.

A New Waters Hole Detection and Tracking Method for UGV in Cross-Country Environment

2016 ◽  
Vol 30 (08) ◽  
pp. 1655024 ◽  
Author(s):  
Yibing Zhao ◽  
Feng Ding ◽  
Xuecai Yu ◽  
Ronghui Zhang ◽  
Xiumei Xiang
Keyword(s):  
Scale Transformation ◽  
Support Vector ◽  
Svm Classifier ◽  
Feature Descriptor ◽  
Feature Points ◽  
Remarkable Feature ◽  
Tracking Method ◽  
Detection And Tracking ◽  
Cross Country ◽  
Hole Detection

Environment perception is one of the important issues for unmanned ground vehicle (UGV). It is necessary to develop waters hole detection and tracking method in cross-country environment. This paper is related to the waters hole detection and tracking by using visual information. Image processing strategies based on support vector machine (SVM) and speeded up robust feature (SURF) methods are employed to detect and track waters hole. It focuses on how to extract the waters feature descriptor by exploring the machine learning algorithm. Based on the S/V color features and Gray Level Co-occurrence Matrix, the waters feature descriptor is extracted. The radial basis function (RBF) kernel function and the sampling-window size are determined by using the SVM classifier. The optimal parameters are obtained under the cross-validation conditions by the grid method. In terms of waters tracking, SURF feature matching method is applied to extract the remarkable feature points, then to observe the relation between feature point movement of adjacent frames and scale change ratio. Experiments show that SURF algorithm can still be effective to detect and match the remarkable feature points, against the negative effects of waters scale transformation and affine transform. The conclusion is that the computing speed of SURF algorithm is about three times faster than that of scale-invariant feature transform (SIFT) algorithm, and the comprehensive performance of SURF algorithm is better.

scholarly journals Landmark Points Detection in Case of Human Facial Tracking and Detection

2019 ◽  
Vol 9 (2) ◽  
pp. 3769-3776
Keyword(s):  
Face Detection ◽  
Error Detection ◽  
Mental States ◽  
Support Vector ◽  
Svm Classifier ◽  
Biometric Recognition ◽  
Essential Point ◽  
Facial Expression Analysis ◽  
Detection And Tracking ◽  
Facial Landmark

This paper describes the human facial landmark points detection is very important in the field of image processing as face detect, face identifies, face re-construct, face corners alignment, different head pose and facial expression analysis. Facial landmark is an essential point for applying face processing operation ranging from biometric recognition to mental states. In this paper, Haar cascading face detection technique is used to face detection and tracking. Histogram of Oriented Gradients (hog) has been used for 68 landmark points detection in case of human tracking and detection and support vector machine (svm) classifier are used for 68 landmark points detection for right-left eyebrow, left-right eye, nose, lips, chin, and jaw. The existing methods work effectively but many issues occur in detection as of different head poses, facial expressions, facial occlusion, illumination, colour, shadowing and self-shadowing etc. The performance of experimental results shows the advantages of our purposed method is highly accurate in terms of facial 68 landmark points tracking and detection and less error detection rate with the Multi-PIE database.

Text Localisation for Roman Words from Shop Signage

2017 ◽  
Vol 14 (2) ◽  
pp. 49
Author(s):  
Nurbaity Sabri ◽  
Noor Hazira Yusof ◽  
Zaidah Ibrahim ◽  
Zolidah Kasiran ◽  
Nur Nabilah Abu Mangshor
Keyword(s):  
Comparative Study ◽  
Kernel Function ◽  
Texture Features ◽  
Text Recognition ◽  
Primary Data ◽  
Support Vector ◽  
Svm Classifier ◽  
Histogram Of Oriented Gradient ◽  
Speeded Up Robust Features ◽  
Better Than

Text localisation determines the location of the text in an image. This process is performed prior to text recognition.  Localising text on shop signage is a challenging task since the images of the shop signage consist of complex background, and the text occurs in various font types, sizes, and colours. Two popular texture features that have been applied to localise text in scene images are a histogram of oriented gradient (HOG) and speeded up robust features (SURF).  A comparative study is conducted in this paper to determine which is better with support vector machine (SVM) classifier. The performance of SVM is influenced by its kernel function and another comparative study is conducted to identify the best kernel function. The experiments have been conducted using primary data collected by the authors. Results indicate that HOG with quadratic kernel function localises text for shop signage better than SURF.

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