Improved vehicle speed estimation using Gaussian mixture model and hole filling algorithm

Nurhadiyatna, Adi; Hardjono, Benny; Wibisono, Ari; Sina, Ibnu; Jatmiko, Wisnu; Ma’sum, M. Anwar; Mursanto, Petrus

doi:10.1109/icacsis.2013.6761617

Cited by 25 publications

(19 citation statements)

References 19 publications

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“…One existing monocular camera-based method to vehicle speed estimation utilizes the technique of Camera Calibration [1,5,6]. In short, this method obtains the algorithm-generated scale and calculates the speed based on the vehicle trajectories acquired by a two-stage process including detection and tracking.…”

Section: Introductionmentioning

confidence: 99%

“…Due to different camera models, shooting angles and installation positions, camera calibration is required for each camera. Some camera calibration-based methods need the multiple manual measurements on the road [5,7,8]. Some algorithms have limitations in camera placement [1,5,9,10].…”

Section: Introductionmentioning

confidence: 99%

“…Some camera calibration-based methods need the multiple manual measurements on the road [5,7,8]. Some algorithms have limitations in camera placement [1,5,9,10]. Besides, the accuracy of the speed estimate heavily relies on the accuracy of algorithms for detection and tracking.…”

Section: Introductionmentioning

confidence: 99%

“…In the first case, the computation is automatic except for the camera calibration part, where manual manipulation required is usually in the form of measuring certain distances in the scene and certain camera parameters. For example, Pin Hole Model [5] requires the camera height from the ground plane is used for camera calibration, and Gaussian Mixture Model and Kalman Filter are used for detection and tracking. Some methods make use of line markings on the road to deal with perspective projection [15][16][17].…”

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confidence: 99%

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Vehicle Speed Estimation Based on 3D ConvNets and Non-Local Blocks

2019

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Vehicle speed estimation is an important problem in traffic surveillance. Many existing approaches to this problem are based on camera calibration. Two shortcomings exist for camera calibration-based methods. First, camera calibration methods are sensitive to the environment, which means the accuracy of the results are compromised in some situations where the environmental condition is not satisfied. Furthermore, camera calibration-based methods rely on vehicle trajectories acquired by a two-stage tracking and detection process. In an effort to overcome these shortcomings, we propose an alternate end-to-end method based on 3-dimensional convolutional networks (3D ConvNets). The proposed method bases average vehicle speed estimation on information from video footage. Our methods are characterized by the following three features. First, we use non-local blocks in our model to better capture spatial–temporal long-range dependency. Second, we use optical flow as an input in the model. Optical flow includes the information on the speed and direction of pixel motion in an image. Third, we construct a multi-scale convolutional network. This network extracts information on various characteristics of vehicles in motion. The proposed method showcases promising experimental results on commonly used dataset with mean absolute error (MAE) as 2.71 km/h and mean square error (MSE) as 14.62 .

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Vehicle Speed Estimation Based on 3D ConvNets and Non-Local Blocks

2019

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“…Each pixel in the frame was compared with model formed from GMM. Pixels with similarity values under the standard deviation and highest weight factor were considered as background, while pixels with higher standard deviation and lower weight factor considered as foreground [7].…”

Section: Gaussian Mixture Model (Gmm)mentioning

confidence: 99%

Vehicle detection and tracking using Gaussian Mixture Model and Kalman Filter

Indrabayu

Bakti

Areni

et al. 2016

2016 International Conference on Computational Intelligence and Cybernetics

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Abstract-Intelligent Transport System (ITS) is a method used in traffic arrangements to make efficient road transport system. One of the ITS application is the detection and tracking of vehicle objects. In this research, Gaussian Mixture Model (GMM) method was applied for vehicle detection and Kalman Filter method was applied for object tracking. The data used are vehicles video under two different conditions. First condition is light traffic and second condition is heavy traffic. Validation of detection system is conducted using Receiver Operating Characteristic (ROC) analysis. The result of this research shows that the light traffic condition gets 100% for the precision value, 94.44% for sensitivity, 100% for specificity, and 97.22% for accuracy. While the heavy traffic condition gets 75.79% for the precision value, 88.89% for sensitivity, 70.37% for specificity, and 79.63% for accuracy. With avarage consistency of Kalman Filter for object tracking is 100%.

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