Lane detection and tracking based on annealed particle filter

Zhao, Hongran; Kim, Onecue; Won, Jong-Seob; Kang, Dong-Joong

doi:10.1007/s12555-013-0279-2

Cited by 12 publications

(4 citation statements)

References 9 publications

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“…This can lead to unwanted lines for various reasons such as insufficient amount of marking lines, blurred images and passing vehicles. These noisy lines with unqualified angles can be removed using K-means clustering technique [51] as shown in Fig. 7a.…”

Section: ) K-means Clusteringmentioning

confidence: 99%

“…K-means clustering algorithm is used to classify edge pixels into two groups: left and right lane. Zhao et al [51] proposed a conventional cluster algorithm to localize the lane lines using a K-means clustering algorithm. In edge pixels, the set of lane markings S is defined by (8).…”

Section: ) K-means Clusteringmentioning

confidence: 99%

“…Lane tracking increases the probability to detect lane markings in poor conditions. Intensive research studies using Kalman Filter (KF) [5], [14], [26] or Particle Filter (PF) [51] have been done. In order to increase the fidelity and the efficiency of lane detection system, KF is used to track both limits of each lane markings extracted by PPHT.…”

Section: Lane Boundary Estimation Using Kalman Filtermentioning

confidence: 99%

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A Lane Tracking Method Based on Progressive Probabilistic Hough Transform

et al. 2020

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Lane departure warning systems have gained considerable research interest in the past decade for its promising usage in automotive, where lane detection and tracking is applied. However, it is a challenging task to improve the robustness of lane detection due to environmental factors, such as perspective effect, possible low visibility of lanes, and partial occlusions. To deal with these issues, we propose a reliable vision-based real-time lane markings detection and tracking system that can adapt to various environmental conditions. The lane detection is composed of three stages: pre-processing, Adaptive Region of Interest (AROI) setting, and lane marking detection and tracking. In the pre-processing stage, smoothing and edge detection operators are applied on input frames to automatically obtain binary images, then, lane markings segmentation are carried out. After that, An Adaptive Region of Interest is extracted to reduce the computational complexity. In the subsequent detection stages, Kalman filter is employed to track road boundaries detected in the AROI using Progressive Probabilistic Hough Transform (PPHT) in the next frame. Based on road boundaries and the vehicle's position, the proposed algorithm decides if the vehicle has drifted off the lane. For the performance evaluation of lane detection and tracking, real-life datasets for both urban roads and highways in various lighting conditions are used. Applying our method to Catltech dataset, the average correct detection rate is 93.82%. In addition, the proposed method outperforms that of the state-of-the-art methods in processing time (21.54ms/frame). INDEX TERMS Advanced driving assistance systems (ADAS), lane detection and tracking, lane departure warning system (LDWS), progressive probabilistic Hough transform (PPHT), adaptive region of interest (AROI).

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Section: ) K-means Clusteringmentioning

confidence: 99%

Section: ) K-means Clusteringmentioning

confidence: 99%

Section: Lane Boundary Estimation Using Kalman Filtermentioning

confidence: 99%

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A Lane Tracking Method Based on Progressive Probabilistic Hough Transform

et al. 2020

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“…With the use of cameras, not only is the cost reduced but also richer information can be acquired in a non-intrusive way without affecting the integrity of the road. Video cameras provide important visual information for various monitoring objectives such as lane detection [2,3], path planning for autonomouse vehicle [4,5] or car localization [6]. These are supported by pattern recognition techniques which have garnered much attention in current research.…”

Section: Introductionmentioning

confidence: 99%

Front-view car detection and counting with occlusion in dense traffic flow

Pham

Lee

2015

Int. J. Control Autom. Syst.

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In dense traffic flow, car occlusion is usually one of the great challenges of vehicle detection and tracking in traffic monitoring systems. Current methods of car hypothesis such as symmetry or shadow based method work only with non-occluded cars. In this paper, we proposed an approach to car detection and counting using a new method of car hypothesis based on car windshield appearance which is the most feasible cue to hypothesize cars in occlusion situations. In hypothesis stage, Hough transformation is used to detect trapezoid-like regions where a car's windshield could be located, and then candidate car regions are estimated by the windshield region and its size. In verification stage, HOG descriptor and a well-collected dataset are used to train a linear SVM classifier for detecting cars at a high accuracy rate. Then, a tracking process based on Kalman filter is used to track the movement of detected cars in consecutive frames of traffic videos, followed by rule-based reasoning for counting decision. Experimental results on real traffic videos showed that the system is able to detect, track and count multiple cars including occlusion in dense traffic flow in real-time.

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