scholarly journals Experimental Evaluation of Vehicle Detection Based on Background Modelling in Daytime and Night-Time Video

2014 ◽  
Author(s):  
Igor Lipovac ◽  
Tomislav Hrkać ◽  
Karla Brkić ◽  
Zoran Kalafatić ◽  
Siniša Šegvić
Keyword(s):  
Experimental Evaluation ◽  
Vehicle Detection ◽  
Night Time ◽  
Background Modelling

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.

scholarly journals A Real-Time Embedded Blind Spot Safety Assistance System

2012 ◽  
Vol 2012 ◽  
pp. 1-15 ◽  
Author(s):  
Bing-Fei Wu ◽  
Chih-Chung Kao ◽  
Ying-Feng Li ◽  
Min-Yu Tsai
Keyword(s):  
Real Time ◽  
Detection System ◽  
Vehicle Detection ◽  
Blind Spot ◽  
Assistance System ◽  
Night Time ◽  
Spot Area ◽  
Temporal Features ◽  
Detection Approach ◽  
Edge Features

This paper presents an effective vehicle and motorcycle detection system in the blind spot area in the daytime and nighttime scenes. The proposed method identifies vehicle and motorcycle by detecting the shadow and the edge features in the daytime, and the vehicle and motorcycle could be detected through locating the headlights at nighttime. First, shadow segmentation is performed to briefly locate the position of the vehicle. Then, the vertical and horizontal edges are utilized to verify the existence of the vehicle. After that, tracking procedure is operated to track the same vehicle in the consecutive frames. Finally, the driving behavior is judged by the trajectory. Second, the lamps in the nighttime are extracted based on automatic histogram thresholding, and are verified by spatial and temporal features to against the reflection of the pavement. The proposed real-time vision-based Blind Spot Safety-Assistance System has implemented and evaluated on a TI DM6437 platform to perform the vehicle detection on real highway, expressways, and urban roadways, and works well on sunny, cloudy, and rainy conditions in daytime and night time. Experimental results demonstrate that the proposed vehicle detection approach is effective and feasible in various environments.

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