Night-Time Traffic Surveillance: A Robust Framework for Multi-vehicle Detection, Classification and Tracking

2009 ◽  
Author(s):  
Kostia Robert
Keyword(s):  
Vehicle Detection ◽  
Traffic Surveillance ◽  
Night Time ◽  
Robust Framework

scholarly journals Vehicle pose estimation for vehicle detection and tracking based on road direction

2017 ◽  
Vol 3 (1) ◽  
pp. 35
Author(s):  
Adhi Prahara ◽  
Ahmad Azhari ◽  
Murinto Murinto
Keyword(s):  
Vehicle Detection ◽  
Training Data ◽  
Support Vector ◽  
Traffic Surveillance ◽  
Vehicle Performance ◽  
Surveillance Camera ◽  
Detection And Tracking ◽  
Vehicle Detection And Tracking ◽  
Different Color ◽  
Linear Svm

Vehicle has several types and each of them has different color, size, and shape. The appearance of vehicle also changes if viewed from different viewpoint of traffic surveillance camera. This situation can create many possibilities of vehicle poses. However, the one in common, vehicle pose usually follows road direction. Therefore, this research proposes a method to estimate the pose of vehicle for vehicle detection and tracking based on road direction. Vehicle training data are generated from 3D vehicle models in four-pair orientation categories. Histogram of Oriented Gradients (HOG) and Linear-Support Vector Machine (Linear-SVM) are used to build vehicle detectors from the data. Road area is extracted from traffic surveillance image to localize the detection area. The pose of vehicle which estimated based on road direction will be used to select a suitable vehicle detector for vehicle detection process. To obtain the final vehicle object, vehicle line checking method is applied to the vehicle detection result. Finally, vehicle tracking is performed to give label on each vehicle. The test conducted on various viewpoints of traffic surveillance camera shows that the method effectively detects and tracks vehicle by estimating the pose of vehicle. Performance evaluation of the proposed method shows 0.9170 of accuracy and 0.9161 of balance accuracy (BAC).

A hierarchical clustering of features approach for vehicle tracking in traffic environments

2016 ◽  
Vol 9 (4) ◽  
pp. 354-368 ◽  
Author(s):  
Anan Banharnsakun ◽  
Supannee Tanathong
Keyword(s):  
Hierarchical Clustering ◽  
Moving Objects ◽  
Vehicle Detection ◽  
Corner Detection ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Night Time ◽  
Content Type ◽  
Traffic Conditions ◽  
Detection And Tracking

Purpose Developing algorithms for automated detection and tracking of multiple objects is one challenge in the field of object tracking. Especially in a traffic video monitoring system, vehicle detection is an essential and challenging task. In the previous studies, many vehicle detection methods have been presented. These proposed approaches mostly used either motion information or characteristic information to detect vehicles. Although these methods are effective in detecting vehicles, their detection accuracy still needs to be improved. Moreover, the headlights and windshields, which are used as the vehicle features for detection in these methods, are easily obscured in some traffic conditions. The paper aims to discuss these issues. Design/methodology/approach First, each frame will be captured from a video sequence and then the background subtraction is performed by using the Mixture-of-Gaussians background model. Next, the Shi-Tomasi corner detection method is employed to extract the feature points from objects of interest in each foreground scene and the hierarchical clustering approach is then applied to cluster and form them into feature blocks. These feature blocks will be used to track the moving objects frame by frame. Findings Using the proposed method, it is possible to detect the vehicles in both day-time and night-time scenarios with a 95 percent accuracy rate and can cope with irrelevant movement (waving trees), which has to be deemed as background. In addition, the proposed method is able to deal with different vehicle shapes such as cars, vans, and motorcycles. Originality/value This paper presents a hierarchical clustering of features approach for multiple vehicles tracking in traffic environments to improve the capability of detection and tracking in case that the vehicle features are obscured in some traffic conditions.

Sign in / Sign up

Export Citation Format

Share Document