A superparticle filter for lane detection

Shin, Bok-Suk; Tao, Junli; Klette, Reinhard

doi:10.1016/j.patcog.2014.10.011

Cited by 57 publications

(21 citation statements)

References 29 publications

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“…Hoang et al [54] use the LSD method to make the proposed system robust to occlusion. Shin et al [19] proposed the use of multiple particle filters for detecting left and right lane borders separately.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

“…detection methods and is about 2% lower than some other methods. It is mostly due to state-of-the-art lane detection methods used sophisticated processing environment such as 2 to 4 cores higher processor clock speed and some methods like Shin et al [19]; Ruyi et al [58] applied tracking stage after detection stage for enhancing lane detection rate. The proposed method achieved comparable results in average lane detection rate at 93.82% with real-life datasets.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

“…Once the ROI is confirmed, some lane detection approaches apply Warp Perspective Mapping (WPM) [19], while others apply Inverse Perspective Mapping (IPM) [7], [20] to get the bird's eye view of the road image based on the assumption of parallel lane boundaries. Although, the effective detection portion, such as the lane, can account for approximately two-thirds of the area of the image, it may still contains noise.…”

Section: Related Workmentioning

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: Comparison With Other Methodsmentioning

confidence: 99%

Section: Comparison With Other Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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A Lane Tracking Method Based on Progressive Probabilistic Hough Transform

et al. 2020

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“…For lane tracking, Kalman filter and particle filter were two most widely used tracking algorithms [4]. Shin proposed a super-particle filter combining two separate particle filters for ego lane boundary tracking [48]. In [49], a learning-based lane detection method was proposed and tracked with a particle filter.…”

Section: B Algorithm Level Integrationmentioning

confidence: 99%

Advances in Vision-Based Lane Detection: Algorithms, Integration, Assessment, and Perspectives on ACP-Based Parallel Vision

Xing

Chen

Wang

et al. 2018

IEEE/CAA J. Autom. Sinica

137

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Lane detection is a fundamental aspect of most current advanced driver assistance systems (ADASs). A large number of existing results focus on the study of vision-based lane detection methods due to the extensive knowledge background and the low-cost of camera devices. In this paper, previous visionbased lane detection studies are reviewed in terms of three aspects, which are lane detection algorithms, integration, and evaluation methods. Next, considering the inevitable limitations that exist in the camera-based lane detection system, the system integration methodologies for constructing more robust detection systems are reviewed and analyzed. The integration methods are further divided into three levels, namely, algorithm, system, and sensor. Algorithm level combines different lane detection algorithms while system level integrates other object detection systems to comprehensively detect lane positions. Sensor level uses multi-modal sensors to build a robust lane recognition system. In view of the complexity of evaluating the detection system, and the lack of common evaluation procedure and uniform metrics in past studies, the existing evaluation methods and metrics are analyzed and classified to propose a better evaluation of the lane detection system. Next, a comparison of representative studies is performed. Finally, a discussion on the limitations of current lane detection systems and the future developing trends toward an Artificial Society, Computational experiment-based parallel lane detection framework is proposed.

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“…The evaluation is performed visually as follows [8]: For every frame where objects on the water surface are present. Object detection result for every frames is classified as detected bounding box b being either correct or not correct accordingly, assigned numeric values (b) is in the set {0, 1} respectively.…”

Section: A Performance Evaluationmentioning

confidence: 99%

Vision based long range object detection and tracking for unmanned surface vehicle

Wang

Mou

et al. 2015

2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation

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A real time vision based long range object detection and tracking algorithm for unmanned surface vehicles (USV) is proposed in this paper. HD image (2736 × 2192) is utilised in this work to obtain high accuracy for the object distance estimation. With handling such high resolution images for real time performance, we propose a coarse to fine approach, which firstly estimates the sea surface plane and locations of objects coarsely on lower resolution images corresponding to the HD images, then the detected coarse locations or regions of interest (ROI) are projected to the original HD image, finally stereo matching is preformed in the original image only on these extracted ROI, which renders more accurate 3D information for localizing the objects on the open sea. In the tracking, we propose to combine the target tracking based on 2D image with the constrained template matching to compute the depth, which demonstrates a more robust and accurate performance. Experimental results with our own dataset verify the high efficiency of our proposed method.

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A superparticle filter for lane detection

Cited by 57 publications

References 29 publications

A Lane Tracking Method Based on Progressive Probabilistic Hough Transform

A Lane Tracking Method Based on Progressive Probabilistic Hough Transform

Advances in Vision-Based Lane Detection: Algorithms, Integration, Assessment, and Perspectives on ACP-Based Parallel Vision

Vision based long range object detection and tracking for unmanned surface vehicle

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