Feature Selection Based on Tensor Decomposition and Object Proposal for Night-Time Multiclass Vehicle Detection

Hulin Kuang; Long Chen; Leanne Lai Hang Chan; Ray C. C. Cheung; Hong Yan

doi:10.1109/tsmc.2018.2872891

Feature Selection Based on Tensor Decomposition and Object Proposal for Night-Time Multiclass Vehicle Detection

IEEE Transactions on Systems Man and Cybernetics Systems ◽

10.1109/tsmc.2018.2872891 ◽

2019 ◽

Vol 49 (1) ◽

pp. 71-80 ◽

Cited By ~ 4

Author(s):

Hulin Kuang ◽

Long Chen ◽

Leanne Lai Hang Chan ◽

Ray C. C. Cheung ◽

Hong Yan

Keyword(s):

Feature Selection ◽

Tensor Decomposition ◽

Vehicle Detection ◽

Night Time ◽

Object Proposal

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An automated night-time vehicle detection system for driving assistance based on cross-correlation

2019 International Conference on Systems of Collaboration Big Data, Internet of Things & Security (SysCoBIoTS) ◽

10.1109/syscobiots48768.2019.9028038 ◽

2019 ◽

Author(s):

Abdelmoghit Zaarane ◽

Ibtissam Slimani ◽

Wahban Al Okaishi ◽

Issam Atouf ◽

Abdellatif Hamdoun

Keyword(s):

Cross Correlation ◽

Detection System ◽

Vehicle Detection ◽

Night Time ◽

Driving Assistance

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

International Journal of Intelligent Computing and Cybernetics ◽

10.1108/ijicc-08-2015-0027 ◽

2016 ◽

Vol 9 (4) ◽

pp. 354-368 ◽

Cited By ~ 3

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.

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