scholarly journals Vehicle Detection, Tracking and Counting Using Gaussian Mixture Model and Optical Flow

2020 ◽  
pp. 19-27
Author(s):  
Muhammad Moin Akhtar ◽  
Yong Li ◽  
Lei Zhong ◽  
Ayesha Ansari
Keyword(s):  
Optical Flow ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Traffic Congestion ◽  
Moving Objects ◽  
Vehicle Detection ◽  
Gaussian Mixture ◽  
Intelligent Transport System ◽  
Moving Vehicle ◽  
Optical Flow Method

Vehicle detection, tracking, and counting play a significant role in traffic surveillance and are principle applications of the Intelligent Transport System (ITS). Traffic congestion and accidents can be prevented with an adequate solution to problems. In this paper, we implemented different image processing techniques to detect and track the moving vehicle from the videos captured by a stationary camera and count the total number of vehicles passed by. The proposed approach consists of an optical flow method with a Gaussian mixture model (GMM) to obtain an absolute shape of particular moving objects which improves the detection performance of moving targets.

Moving Objects Detection Algorithm Based on Three-Dimensional Gaussian Mixture Codebook Model Using XYZ Color Model

2013 ◽  
Vol 347-350 ◽  
pp. 3505-3509 ◽  
Author(s):  
Jin Huang ◽  
Wei Dong Jin ◽  
Na Qin
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Moving Objects ◽  
Three Dimensional ◽  
Gaussian Mixture ◽  
Detection Algorithm ◽  
Color Model ◽  
Moving Objects Detection ◽  
Objects Detection ◽  
Model Algorithm

In order to reduce the difficulty of adjusting parameters for the codebook model and the computational complexity of probability distribution for the Gaussian mixture model in intelligent visual surveillance, a moving objects detection algorithm based on three-dimensional Gaussian mixture codebook model using XYZ color model is proposed. In this algorithm, a codebook model based on XYZ color model is built, and then the Gaussian model based on X, Y and Z components in codewords is established respectively. In this way, the characteristic of the three-dimensional Gaussian mixture model for the codebook model is obtained. The experimental results show that the proposed algorithm can attain higher real-time capability and its average frame rate is about 16.7 frames per second, while it is about 8.3 frames per second for the iGMM (improved Gaussian mixture model) algorithm, about 6.1 frames per second for the BM (Bayes model) algorithm, about 12.5 frames per second for the GCBM (Gaussian-based codebook model) algorithm, and about 8.5 frames per second for the CBM (codebook model) algorithm in the comparative experiments. Furthermore the proposed algorithm can obtain better detection quantity.

Algorithm of Moving Vehicle Detection by Improved Gaussian Mixture Model Based on ROI

2015 ◽  
Vol 734 ◽  
pp. 463-467 ◽  
Author(s):  
Pan Pan Zhang ◽  
Chun Yang Mu ◽  
Xing Ma ◽  
Fu Lu Xu
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Region Of Interest ◽  
Gaussian Mixture ◽  
Background Model ◽  
Morphological Filter ◽  
Improved Method ◽  
Moving Vehicle ◽  
Model Based ◽  
Moving Vehicle Detection

Detection of moving object is a hot topic in computer vision. Traditionally, it is detected for every pixel in whole image by Gaussian mixture background model, which may waste more time and space. In order to improving the computational efficiency, an advanced Gaussian mixture model based on Region of Interest was proposed. Firstly, the solution finds out the most probably region where the target may turn up. And then Gaussian mixture background model is built in this area. Finally, morphological filter algorithm is used for improving integrity of the detected targets. Results show that the improved method could have a more perfect detection but no more time increasing than typical method.

scholarly journals Efficient Moving Vehicle Detection Algorithm for Various Traffic Conditions

2019 ◽  
Vol 8 (3) ◽  
pp. 6069-6076
Keyword(s):  
Real Time ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Traffic Monitoring ◽  
Visual Surveillance ◽  
Detection Accuracy ◽  
Data Set ◽  
Moving Vehicle ◽  
Detection Techniques

Many computer vision applications needs to detect moving object from an input video sequences. The main applications of this are traffic monitoring, visual surveillance, people tracking and security etc. Among these, traffic monitoring is one of the most difficult tasks in real time video processing. Many algorithms are introduced to monitor traffic accurately. But most of the cases, the detection accuracy is very less and the detection time is higher which makes the algorithms are not suitable for real time applications. In this paper, a new technique to detect moving vehicle efficiently using Modified Gaussian Mixture Model and Modified Blob Detection techniques is proposed. The modified Gaussian Mixture model generates the background from overall probability of the complete data set and by calculating the required step size from the frame differences. The modified Blob Analysis is then used to classify proper moving objects. The simulation results shows that the method accurately detect the target

scholarly journals Moving Vehicle Detection and Tracking Based on Optical Flow Method and Immune Particle Filter under Complex Transportation Environments

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Wei Sun ◽  
Min Sun ◽  
Xiaorui Zhang ◽  
Mian Li
Keyword(s):  
Optical Flow ◽  
Particle Filter ◽  
Vehicle Detection ◽  
Moving Vehicle ◽  
Flow Method ◽  
Optical Flow Method ◽  
Detection And Tracking ◽  
Vehicle Detection And Tracking ◽  
Particle Filter Algorithm ◽  
Moving Vehicle Detection

Video-based moving vehicle detection and tracking is an important prerequisite for vehicle counting under complex transportation environments. However, in the complex natural scene, the conventional optical flow method cannot accurately detect the boundary of the moving vehicle due to the generation of the shadow. In addition, traditional vehicle tracking algorithms are often occluded by trees, buildings, etc., and particle filters are also susceptible to particle degradation. To solve this problem, this paper proposes a kind of moving vehicle detection and tracking based on the optical flow method and immune particle filter algorithm. The proposed method firstly uses the optical flow method to roughly detect the moving vehicle and then uses the shadow detection algorithm based on the HSV color space to mark the shadow position after threshold segmentation and further combines the region-labeling algorithm to realize the shadow removal and accurately detect the moving vehicle. Improved affinity calculation and mutation function of antibody are proposed to make the particle filter algorithm have certain adaptivity and robustness to scene interference. Experiments are carried out in complex traffic scenes with shadow and occlusion interference. The experimental results show that the proposed algorithm can well solve the interference of shadow and occlusion and realize accurate detection and robust tracking of moving vehicles under complex transportation environments, which has the potentiality to be processed on a cloud computing platform.

Object Modeling Using Gaussian Mixture Model for Infrared Image and its Application to Vehicle Detection

2006 ◽  
Vol 18 (6) ◽  
pp. 738-743 ◽  
Author(s):  
Makito Seki ◽  
◽  
Haruhisa Okuda ◽  
Manabu Hashimoto ◽  
Nami Hirata
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Infrared Image ◽  
Vehicle Detection ◽  
Gaussian Mixture ◽  
Modeling Method ◽  
Object Modeling ◽  
Visible Image ◽  
Low Contrast ◽  
Gradient Orientation

In this paper, we propose a new object modeling method for infrared (IR) image. It is based on the modeling method using Gaussian Mixture Model (GMM) that has been originally proposed for general visible image. The original method is one of effective object modeling algorithms that can describe the topological structures of the internal patterns of object. This approach can also eliminate the influences due to small differences between patterns. On the other hand, an IR image is often used instead of visible image in actual applications such as outdoor surveillance. IR images make it easy to extract foreground object regions from background scenes, but their low-contrast makes object modeling difficult. We therefore propose a modeling method using Orientation-Code for IR image. Orientation-Code of each pixel has information about the maximum-gradient orientation of image, not intensity information. Gradient orientation information does not depend on contrast and describes internal pattern structures of objects even in unclear IR images. We also applied proposed method to vehicle detection for outdoor scenes, where it extracts multiple foreground regions as vehicle candidates using background subtraction for IR image, and they are described as models by our method. Models are finally compared with standard vehicle view models pre-memorized to determine which candidate is true vehicle or not. Evaluation tests with actual IR video sequences have proved that our proposed algorithm detects objects robustly.

Sign in / Sign up

Export Citation Format

Share Document