scholarly journals Motion Tracking and Testing Based on Improved Surendra Algorithm

2014 ◽  
Vol 8 (1) ◽  
pp. 620-624
Author(s):  
Ji Yao ◽  
Deepa Singh
Keyword(s):  
Computer Vision ◽  
System Design ◽  
Target Detection ◽  
Target Vehicle ◽  
The Road ◽  
Detection And Tracking ◽  
Frame Difference ◽  
Adjacent Frame ◽  
On The Road ◽  
Tracking Technology

Recently, due to the gradual mature of the development of computer vision, video-based monitoring and control system has become a classic practice in the field of computer vision. Traffic detection and tracking technology in intelligent video surveillance system is one of the branches of computer vision, which has gradually become a hot and new research field. Through analysis and summary of the existing detection and tracking technology, this study draws a set of target detection and tracking program at the perspective of taking photos with a single fixed camera on the road. The target in the program is the vehicle on the road. The key point of the program is to detect the target, and another is tracking. The main purpose of this study is to detect and track the moving vehicles on the road in the condition of a single fixed camera. This detection program uses the improved surendra algorithm, which is a more advanced algorithm in the algorithms of moving target detection. In all the algorithms, such as background subtraction method and the adjacent frame difference method, the improved surendra algorithm is more excellent than them. The algorithm is based on the mixed Gaussian model method and the improved adjacent frame difference. Experiment shows that the algorithm is able to track and detect the target vehicle accurately indeed. And the complexity, real-time and robustness of the algorithm are very consistent with the system design requirements of the study, so the adoption of the algorithm and the implementation of the detection system design of this study can track and detect the target vehicle well.

Vehicle Detection and Tracking in Complex Traffic Circumstances with Roadside LiDAR

2019 ◽  
Vol 2673 (9) ◽  
pp. 62-71 ◽  
Author(s):  
Zhenyao Zhang ◽  
Jianying Zheng ◽  
Hao Xu ◽  
Xiang Wang
Keyword(s):  
High Resolution ◽  
Traffic Safety ◽  
Traffic Accidents ◽  
Vehicle Detection ◽  
Traffic Data ◽  
The Road ◽  
Detection And Tracking ◽  
Area Of Interest ◽  
Vehicle Detection And Tracking ◽  
On The Road

The problem of traffic safety has become increasingly prominent owing to the increase in the number of cars. Traffic accidents often occur in an instant, which makes it necessary to obtain traffic data with high resolution. High-resolution micro traffic data (HRMTD) indicates that the spatial resolution reaches the centimeter level and that the temporal resolution reaches the millisecond level. The position, direction, speed, and acceleration of objects on the road can be extracted with HRMTD. In this paper, a LiDAR sensor was installed at the roadside for data collection. An adjacent-frame fusion method for vehicle detection and tracking in complex traffic circumstances is presented. Compared with the previous research, objects can be detected and tracked without object model extraction or a bounding box description. In addition, problems caused by occlusion can be improved using adjacent frames fusion in the vehicle detection and tracking algorithms in this paper. The data processing procedure are as follows: selection of area of interest, ground point removal, vehicle clustering, and vehicle tracking. The algorithm has been tested at different sites (in Reno and Suzhou), and the results demonstrate that the algorithm can perform well in both simple and complex application scenarios.

scholarly journals APPLYING THE SIGN LUMINANCE COMPUTATION MODEL TO STUDY THE EFFECTS OF OTHER VEHICLES ON SIGN LUMINANCE

Transport ◽  
2014 ◽  
Vol 29 (2) ◽  
pp. 115-124 ◽  
Author(s):  
Fan Ye ◽  
Paul J. Carlson ◽  
Bradford K. Brimley
Keyword(s):  
Calculation Model ◽  
Computation Model ◽  
Target Vehicle ◽  
The Road ◽  
Vehicle Type ◽  
Minimum Distances ◽  
On The Road ◽  
Sign Type ◽  
Comprehensive Study

As one external lighting source on the road, headlamps from adjacent vehicles in the stream traffic should not be ignored. No comprehensive study has yet been developed for exploring the influence of sign luminance produced by other vehicle headlamps. In this paper, a luminance calculation model is developed to calculate sign luminance from all potential headlamps in the stream traffic. Using the model, four main scenarios have been simulated to analyze the effects of the positions of the target vehicle and other vehicles, vehicle type, sign type and sheeting material on the sign luminance. In addition, occlusion between vehicles is also addressed in the paper, by calculating the minimum distances between vehicles for the headlamps and for the driver’s view of the following vehicle when vehicles and the sign are and are not in the same lane.

scholarly journals Determination of the Dynamic Vehicle Model Parameters by Means of Computer Vision

2019 ◽  
Vol 21 (3) ◽  
pp. 28-34
Author(s):  
Daniil A. Loktev ◽  
Alexey A. Loktev ◽  
Alexandra V. Salnikova ◽  
Anna A. Shaforostova
Keyword(s):  
Computer Vision ◽  
Road Traffic ◽  
Deformable Body ◽  
Model Parameters ◽  
Vehicle Model ◽  
The Road ◽  
Complex Approach ◽  
On The Road ◽  
The Individual

This study is devoted to determining the geometric, kinematic and dynamic characteristics of a vehicle. To this purpose, it is proposed to use a complex approach applying the models of deformable body mechanics for describing the oscillatory movements of a vehicle and the computer vision algorithms for processing a series of object images to determine the state parameters of a vehicle on the road. The model of the vehicle vertical oscillations is produced by means of the viscoelastic elements and the dry friction element that fully enough represent the behavior of the sprung masses. The introduced algorithms and models can be used as a part of a complex system for monitoring and controlling the road traffic. In addition, they can determine both the speed of the car and its dynamic parameters and the driving behavior of the individual drivers.

scholarly journals Perancangan E-Commerce Berbasis Web Pada PT. Touch Technology Indonesia

2019 ◽  
Vol 2 (4) ◽  
pp. 141
Author(s):  
Ahmad Saubani ◽  
Esron Rikardo Nainggolan ◽  
Siti Nur Khasanah
Keyword(s):  
Data Collection ◽  
System Design ◽  
Programming Language ◽  
Application Development ◽  
The Road ◽  
Development Tools ◽  
Library Studies ◽  
On The Road ◽  
To Come ◽  
Product Sales

Sales in this case in the form of product sales is one of the important activities for the development of the company and is a very important aspect for the company. Problems a rising in the company regarding the promotion and sales, because it still uses manually, visit home for promotion and provide brochures on the road side, System design used with a waterfall methode, while the data collection techniques use research methods with observation, interviews, and library studies. And database application development tools use MySQL and PHP programming language by using Laravel framework. The purpose of this research is to design a sales system evenly and ease of transaction customers without having to come to physical stores directly. The result of this research is to provide an alternative sales and promotion. It is application created can create increase in company sales.

scholarly journals Visual Sequence Algorithm for Moving Object Tracking and Detection in Images

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Renzheng Xue ◽  
Ming Liu ◽  
Xiaokun Yu
Keyword(s):  
Target Detection ◽  
Difference Method ◽  
Moving Objects ◽  
Target Location ◽  
Mean Shift ◽  
Detection Algorithm ◽  
Moving Target ◽  
Size Change ◽  
Detection And Tracking ◽  
Frame Difference

Objective. The effects of different algorithms on detecting and tracking moving objects in images based on computer vision technology are studied, and the best algorithm scheme is confirmed. Methods. An automatic moving target detection and tracking algorithm based on the improved frame difference method and mean-shift was proposed to test whether the improved algorithm has improved the detection and tracking effect of moving targets. The algorithm improves the traditional three-frame difference method and introduces a single Gaussian background model to participate in target detection. The improved frame difference method is used to detect the target, and the position window and center of the target are determined. Combined with the mean-shift algorithm, it is determined whether the template needs to be updated according to whether it exceeds the set threshold so that the algorithm can automatically track the moving target. Results. The position and size of the search window change as the target location and size change. The Bhattacharyya similarity measure ρ (y) exceeds the threshold r, and the target detection algorithm is successfully restarted. Conclusion. The algorithm for automatic detection and tracking of moving objects based on the improved frame difference method and mean-shift is fast and has high accuracy.

A Human Target Detection and Tracking Method Based on Adaptive Difference and GVF-Snake Algorithm

2013 ◽  
Vol 455 ◽  
pp. 344-349
Author(s):  
Xue Zhang ◽  
Wei Cheng Xie ◽  
Chao Huang ◽  
Qiang Xu
Keyword(s):  
Target Detection ◽  
Detection Algorithm ◽  
Initial Contour ◽  
Differential Detection ◽  
Body Contour ◽  
Snake Model ◽  
Tracking Method ◽  
Detection And Tracking ◽  
Frame Difference ◽  
Snake Algorithm

Detection of Frame difference fails when the human target is stationary in course of moving, this paper presents a method based on combination of adaptive difference and GVF-snake algorithm to solve it. Adaptive differential detection algorithm can accurately extract the target contour, and use it as the initial contour of GVF-snake model which cannot automatically extract it after we got the target. In the process of detection and tracking, calculating GVF field of the whole picture consume too much time, so we use the method of sub-region to improve the real-time. The experimental results show that, the algorithm can provide the actual body contour for GVF-snake model, and effectively track whether the target is stationary or moving.

Fire Detecting System Design under the Mainframe Computer Vision

2014 ◽  
Vol 602-605 ◽  
pp. 2038-2040
Author(s):  
Ming Yi Yu
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
System Design ◽  
Neural Network Model ◽  
Error Rate ◽  
Support Vector ◽  
Frame Difference ◽  
Mainframe Computer ◽  
Vector Basis ◽  
Detecting Method

This paper focused on the fire detecting problem. Traditionally, fire was detected based on the frame difference by subtracting the image pixel. When there existed background similar to its flame color and shape in the environment where the fire happened,, the result of frame difference subtracting is not obvious and the algorithm cannot detect the fire problem according to the result. This paper put forward a fire detecting method based on support vector basis algorithm. The experiment indicated that this kind of neural network model achieved precise fire detecting under the background similar to itself, efficiently decreased the detecting error rate, and obtained satisfactory results.

Improved Vision Based Driving Space Analysis by Condensation of Image Data

2012 ◽  
Vol 605-607 ◽  
pp. 2260-2264
Author(s):  
Yan Fen Mao ◽  
Hans Wiedmann ◽  
Ming Chen
Keyword(s):  
Image Data ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
The Road ◽  
Detection And Tracking ◽  
Detection And Identification ◽  
Diversity Sampling ◽  
Assistance Systems ◽  
On The Road ◽  
Improve Reliability

Sophisticated ADAS (Advanced Driver Assistance Systems) use vision based methods for detection and keeping track of ahead driving cars. With thus acquired data it is possible to implement e.g. following up functions to automatically keep equal distance to ahead driving vehicles or avoid collisions with obstacles ahead. Known vision based methods for detection and tracking of vehicles use its underneath shadow on the road. The main drawbacks of those methods are the detection and identification of a shadow belonging to a vehicle is neither reliable nor robust, and the thereto required processing of the camera images is very expensive concerning processing time. To improve reliability and detectability we propose here to use an approach which is different from the known methods a nonparametric one; to improve processing speed we propose to apply diversity-sampling to condense the image data before processing it.

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