Forward vehicle detection using cluster-based AdaBoost

Baek, Yeul-Min; Kim, Whoi-Yul

doi:10.1117/1.oe.53.10.102103

Cited by 7 publications

(3 citation statements)

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“…Shiue et al [13] described vehicles with fixed features, and developed a detection model with invariant features. Baek et al [14] trained the detection model with adaptive boosting (AdaBoost) algorithm, and detected moving vehicles in the binary graph.…”

Section: Moving Vehicle Detection Methodsmentioning

confidence: 99%

Moving Vehicle Detection and Tracking Based on Video Sequences

Wang¹

2020

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Vehicle detection and tracking are key aspects of intelligent transportation. However, the accuracy of traditional methods for vehicle detection and tracking is severely suppressed by the complexity of road conditions, occlusion, and illumination changes. To solve the problem, this paper initializes the background model based on multiple frames with a fixed interval, and then establishes a moving vehicle detection algorithm based on trust interval. The established algorithm can easily evaluate the background complexity based on regional information. After that, the authors pointed out that the correlation filtering, a classical vehicle tracking algorithm, cannot adapt to the scale changes of vehicles, due to the weak dependence of background information. Hence, a tracking algorithm that adapts to vehicle scale was designed based on background information. Finally, the proposed algorithms were proved feasible for detection and tracking moving vehicles in complex environments. The research provides a good reference for the application of computer vision in moving target detection.

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Section: Moving Vehicle Detection Methodsmentioning

confidence: 99%

Moving Vehicle Detection and Tracking Based on Video Sequences

Wang¹

2020

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“…The artificial neural network method can effectuate convergence of the training set to a local optimum and has been used for vehicle detection [5]. However, this method has fallen somewhat out of favour because many researchers have moved toward classifiers that are able to converge to a global optimum such as support vector machines (SVM) [6] and Adaboost [7]. SVM and Adaboost are usually combined with HOG features [8], edge features [9], or Haar‐like and Gabor features [10] for vehicle detection.…”

Section: Introductionmentioning

confidence: 99%

Real‐time vehicle detection with foreground‐based cascade classifier

Zhuang

Kang

2016

IET Image Processing

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“…However, on the contrary, the high cost, low spatial resolution, narrow field of view and unitary information have restricted their use in practical applications. In recent years, camera sensors are being widely used due to the reasons mentioned above [4,5]. This method overcomes the limitations of active sensors and furthermore can be used in a wider range of applications, such as in lane departure warning systems and event video recorders.…”

Section: Introductionmentioning

confidence: 99%

Improved Genetic Algorithm Optimization for Forward Vehicle Detection Problems

et al. 2015

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Automated forward vehicle detection is an integral component of many advanced driver-assistance systems. The method based on multi-visual information fusion, with its exclusive advantages, has become one of the important topics in this research field. During the whole detection process, there are two key points that should to be resolved. One is to find the robust features for identification and the other is to apply an efficient algorithm for training the model designed with multi-information. This paper presents an adaptive SVM (Support Vector Machine) model to detect vehicle with range estimation using an on-board camera. Due to the extrinsic factors such as shadows and illumination, we pay more attention to enhancing the system with several robust features extracted from a real driving environment. Then, with the introduction of an improved genetic algorithm, the features are fused efficiently by the proposed SVM model. In order to apply the model in the forward collision warning system, longitudinal distance information is provided simultaneously. The proposed method is successfully implemented on a test car and evaluation experimental results show reliability in terms of both the detection rate and potential effectiveness in a real-driving environment.

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Forward vehicle detection using cluster-based AdaBoost

Cited by 7 publications

References 0 publications

Moving Vehicle Detection and Tracking Based on Video Sequences

Moving Vehicle Detection and Tracking Based on Video Sequences

Real‐time vehicle detection with foreground‐based cascade classifier

Improved Genetic Algorithm Optimization for Forward Vehicle Detection Problems

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