Car detection using multi-feature selection for varying poses

Son, Tran Thai; Mita, Seiichi

doi:10.1109/ivs.2009.5164330

Cited by 14 publications

(8 citation statements)

References 14 publications

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“…AdaBoost was proposed to classify the symmetry feature and edge feature in [112] and [113], respectively. The Haar-like feature and AdaBoost classification has been applied to detect vehicles [114], [115].…”

Section: A Vehicle Detectionmentioning

confidence: 99%

Overview of Environment Perception for Intelligent Vehicles

Zhu

Yuen

Mihaylova

et al. 2017

IEEE Trans. Intell. Transport. Syst.

294

125

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Abstract-This paper presents a comprehensive literature review on environment perception for intelligent vehicles. The state-of-the-art algorithms and modeling methods for intelligent vehicles are given, with a summary of their pros and cons. A special attention is paid to methods for lane and road detection, traffic sign recognition, vehicle tracking, behavior analysis, and scene understanding. In addition, we provide information about datasets, common performance analysis, and perspectives on future research directions in this area.Index Terms-Intelligent vehicles, environment perception and modeling, lane and road detection, traffic sign recognition, vehicle tracking and behavior analysis, scene understanding.

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Section: A Vehicle Detectionmentioning

confidence: 99%

Overview of Environment Perception for Intelligent Vehicles

Zhu

Yuen

Mihaylova

et al. 2017

IEEE Trans. Intell. Transport. Syst.

294

125

View full text Add to dashboard Cite

show abstract

“…To estimate the congestion density the method has to determine the volume of vehicles on the road. Many of the approaches [7][8][9] discussed vehicle detection as a single entity, but none involve the counting of vehicles. Detecting vehicles in congestion is a very challenging problem because of the variety of vehicles in terms of shape and color.…”

Section: IImentioning

confidence: 99%

Real time traffic congestion detection system

Nidhal

Ngah

Ismail³

2014

2014 5th International Conference on Intelligent and Advanced Systems (ICIAS)

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In recent years, vehicle management systems have been expanded to include more fields and features. One important system which requires attention is the traffic congestion alert. All previous works on traffic congestion detection either need prior knowledge or lengthy time to detect and recognize the presence of congestion. Some other methods involve huge infrastructure in order to implement. The proposed system in this paper offers a novel method to detect congestion in real-time without any human supervision or any prior knowledge. The designed system counts the vehicles on road by detecting and paring the vehicles backlights from a real time captured image, whereas the system consumes shorter time and maybe feasible to be implemented at any highway premise. The experimental results show that the system has very high detection accuracy (99-100) %.

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“…The traditional machine learning method extracts vehicle features by feature extraction operators such as histogram of oriented gradient (HOG) [9], Haar-like features [10], etc., and inputs the features into a classifier such as support vector machine (SVM) [11], AdaBoost [12], etc. [13][14][15][16]. However, these methods design features manually, the design process is subjective, there is a lack of theoretical guidance, and the generalization ability is poor [17,18].…”

Section: Introductionmentioning

confidence: 99%

Preceding Vehicle Detection Using Faster R-CNN Based on Speed Classification Random Anchor and Q-Square Penalty Coefficient

Cui

Wang

et al. 2019

Electronics

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At present, preceding vehicle detection remains a challenging problem for autonomous vehicle technologies. In recent years, deep learning has been shown to be successful for vehicle detection, such as the faster region with a convolutional neural network (Faster R-CNN). However, when the host vehicle speed increases or there is an occlusion in front, the performance of the Faster R-CNN algorithm usually degrades. To obtain better performance on preceding vehicle detection when the speed of the host vehicle changes, a speed classification random anchor (SCRA) method is proposed. The reasons for degraded detection accuracy when the host vehicle speed increases are analyzed, and the factor of vehicle speed is introduced to redesign the anchors. Redesigned anchors can adapt to changes of the preceding vehicle size rule when the host vehicle speed increases. Furthermore, to achieve better performance on occluded vehicles, a Q-square penalty coefficient (Q-SPC) method is proposed to optimize the Faster R-CNN algorithm. The experimental validation results show that compared with the Faster R-CNN algorithm, the SCRA and Q-SPC methods have certain significance for improving preceding vehicle detection accuracy.

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Car detection using multi-feature selection for varying poses

Cited by 14 publications

References 14 publications

Overview of Environment Perception for Intelligent Vehicles

Overview of Environment Perception for Intelligent Vehicles

Real time traffic congestion detection system

Preceding Vehicle Detection Using Faster R-CNN Based on Speed Classification Random Anchor and Q-Square Penalty Coefficient

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