scholarly journals Real-time vehicle detection and tracking using improved histogram of gradient features and Kalman filters

2018 ◽  
Vol 15 (1) ◽  
pp. 172988141774994 ◽  
Author(s):  
Xinyu Zhang ◽  
Hongbo Gao ◽  
Chong Xue ◽  
Jianhui Zhao ◽  
Yuchao Liu
Keyword(s):  
Kalman Filter ◽  
Intelligent Transportation Systems ◽  
Traffic Management ◽  
Vehicle Detection ◽  
Transportation Systems ◽  
Driver Assistance Systems ◽  
Filter Model ◽  
Detection And Tracking ◽  
Vehicle Detection And Tracking ◽  
Subsequent Motion

Intelligent transportation systems and safety driver-assistance systems are important research topics in the field of transportation and traffic management. This study investigates the key problems in front vehicle detection and tracking based on computer vision. A video of a driven vehicle on an urban structured road is used to predict the subsequent motion of the front vehicle. This study provides the following contributions. (1) A new adaptive threshold segmentation algorithm is presented in the image preprocessing phase. This algorithm is resistant to interference from complex environments. (2) Symmetric computation based on a traditional histogram of gradient (HOG) feature vector is added in the vehicle detection phase. Symmetric HOG feature with AdaBoost classification improves the detection rate of the target vehicle. (3) A motion model based on adaptive Kalman filter is established. Experiments show that the prediction of Kalman filter model provides a reliable region for eliminating the interference of shadows and sharply decreasing the missed rate.

Road Traffic Congestion (TraCo) Estimation Using Multi-Layer Continuous Virtual Loop (MCVL)

2021 ◽  
Vol 17 (2) ◽  
pp. 46-71
Author(s):  
Manipriya Sankaranarayanan ◽  
Mala C. ◽  
Samson Mathew
Keyword(s):  
Intelligent Transportation Systems ◽  
Traffic Congestion ◽  
Traffic Management ◽  
Road Traffic ◽  
Vehicle Detection ◽  
Transportation Systems ◽  
Traffic Information ◽  
Traffic Characteristics ◽  
Traffic Video ◽  
Virtual Loop

Any road traffic management application of intelligent transportation systems (ITS) requires traffic characteristics data such as vehicle density, speed, etc. This paper proposes a robust and novel vehicle detection framework known as multi-layer continuous virtual loop (MCVL) that uses computer vision techniques on road traffic video to estimate traffic characteristics. Estimations of traffic data such as speed, area occupancy and an exclusive spatial feature named as corner detail value (CDV) acquired using MCVL are proposed. Further, the estimation of traffic congestion (TraCo) level using these parameters is also presented. The performances of the entire framework and TraCo estimation are evaluated using several benchmark traffic video datasets and the results are presented. The results show that the improved accuracy in vehicle detection process using MCVL subsequently improves the precision of TraCo estimation. This also means that the proposed framework is well suited to applications that need traffic characteristics to update their traffic information system in real time.

scholarly journals A Multi-Class Multi-Movement Vehicle Counting Framework for Traffic Analysis in Complex Areas Using CCTV Systems

Energies ◽  
2020 ◽  
Vol 13 (8) ◽  
pp. 2036 ◽  
Author(s):  
Khac-Hoai Nam Bui ◽  
Hongsuk Yi ◽  
Jiho Cho
Keyword(s):  
Intelligent Transportation Systems ◽  
State Of The Art ◽  
Traffic Analysis ◽  
Transportation Systems ◽  
Single View ◽  
Detection And Tracking ◽  
Vehicle Detection And Tracking ◽  
Trajectory Approach ◽  
Complex Scenario ◽  
Comprehensive Framework

Traffic analysis using computer vision techniques is attracting more attention for the development of intelligent transportation systems. Consequently, counting traffic volume based on the CCTV system is one of the main applications. However, this issue is still a challenging task, especially in the case of complex areas that involve many vehicle movements. This study performs an investigation of how to improve video-based vehicle counting for traffic analysis. Specifically, we propose a comprehensive framework with multiple classes and movements for vehicle counting. In particular, we first adopt state-of-the-art deep learning methods for vehicle detection and tracking. Then, an appropriate trajectory approach for monitoring the movements of vehicles using distinguished regions tracking is presented in order to improve the performance of the counting. Regarding the experiment, we collect and pre-process the CCTV data at a complex intersection to evaluate our proposed framework. In particular, the implementation indicates the promising results of our proposed method, which achieve accuracy around 80% to 98% for different movements for a very complex scenario with only a single view of the camera.

scholarly journals An Alternative Vehicle Counting Tool Using the Kalman Filter within MATLAB

2017 ◽  
Vol 3 (11) ◽  
pp. 1029 ◽  
Author(s):  
Daphne Espejel-García ◽  
Luis Ricardo Ortíz-Anchondo ◽  
Cornelio Alvarez-Herrera ◽  
Alfonso Hernandez-López ◽  
Vanessa Verónica Espejel-García ◽  
...  
Keyword(s):  
Image Processing ◽  
Kalman Filter ◽  
Random Noise ◽  
Vehicle Detection ◽  
Video Camera ◽  
Transportation Systems ◽  
Magnetic Sensors ◽  
Traffic Information ◽  
Stationary Points ◽  
Vehicle Detection And Tracking

This study proposes an alternative and economical tool to estimate traffic densities, via video-image processing adapting the Kalman filter included in the Matlab code. Traffic information involves acquiring data for long periods of time at stationary points. Vehicle counting is vital in modern transport studies, and can be achieved by using different techniques, such as manual counts, use of pneumatic tubes, magnetic sensors, etc. In this research however, automatic vehicle detection was achieved using image processing, because it is an economical and sometimes even faster option. Commercial automatic vehicle detection and tracking programs/applications already exist, but their use is typically prohibitive due to their high cost. Large cities can obtain traffic recordings from surveillance cameras and process the information, but it is difficult for smaller towns without such infrastructure or even assigned budget. The proposed tool was developed taking into consideration these difficult situations, and it only requires users to have access to a fixed video camera placed at an elevated point (e.g. a pedestrian bridge or a light pole) and a computer with a powerful processor; the images are processed automatically through the Kalman filter code within Matlab. The Kalman filter predicts random signals, separates signals from random noise or detects signals with the presence of noise, minimizing the estimated error. It needs nevertheless some adjustments to focus it for vehicle counting. The proposed algorithm can thus be adapted to fit the users’ necessities and even the camera’s position. The use of this algorithm allows to obtain traffic data and may help small cities´ decision makers dealing with present and future urban planning and the design or installment of transportation systems.

scholarly journals Vehicle detection and tracking for traffic management

2021 ◽  
Vol 10 (1) ◽  
pp. 66
Author(s):  
Mallikarjun Anandhalli ◽  
Vishwanth P. Baligar ◽  
Pavana Baligar ◽  
Pooja Deepsir ◽  
Mithali Iti
Keyword(s):  
Traffic Management ◽  
Vehicle Detection ◽  
Research Area ◽  
Tracking Algorithm ◽  
Video Sequences ◽  
Detection And Tracking ◽  
Vehicle Detection And Tracking ◽  
Corner Points ◽  
Computer Research

<span lang="EN-US">The detection of object with respect to Vehicle and tracking is the most needed step in computer research area as there is wide investment being made form Intelligent Traffic Management. Due to changes in vision or scenes, detection and tracking of vehicle under different drastic conditions has become most challenging process because of the illumination, shadows etc. To overcome this, we propose a method which uses TensorFlow fused with corner points of the vehicles for detection of vehicle and tracking of an vehicle is formulated again, the location of the object which is detected is passed to track the detected object, using the tracking algorithm based on CNN. The proposed algorithm gives result of 90.88% accuracy of detection in video sequences under different conditions of climate.</span>

The Study on the Method of Vehicle Detection and Tracking Based on Gaussian Mixture Model and Kalman Filter

2014 ◽  
Vol 644-650 ◽  
pp. 1266-1269 ◽  
Author(s):  
Li Yuan Lin ◽  
Lin Chen
Keyword(s):  
Kalman Filter ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Vehicle Detection ◽  
Gaussian Mixture ◽  
Detection And Tracking ◽  
Frame Difference ◽  
Vehicle Detection And Tracking ◽  
Target Extraction ◽  
Detection Stage

In the vehicle detection stage,in order to solve the problem of Gaussian Mixture Model having poor adapting capacity on the sudden changes of the illumination,this paper designs a illumination judgement factor.When the factor is greater than a certain threshold,this paper uses three frame difference method for target extraction,otherwise uses the Gaussian Mixture Model.In the vehicle tracking stage,the Kalman Filter is introduced to improve tarcking accuracy and tracking efficiency,at the same time,a tracking list is designed for single and multi objectives tracking.The results show that the method can adapte the sudden changes of the illumination and can achive a better effect of vehicle detection and tracking.

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