Algorithm of Vehicle Trajectory Extraction using Fusion of Hu Moments and Grayscale Feature

Zhou, Jingmei; Zhao, Xiangmo; Xu, Zhijie; Zhou, Zhou

doi:10.3923/itj.2014.1770.1778

Cited by 4 publications

(6 citation statements)

References 4 publications

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“…The smaller the confidence probability is, the less the number of points is. Correct matching points are obtained by using the approach in literature [7] for feature matching method, as shown in Fig.9, where d is the euclidean distances of dx and dy. When the different offsets (dx, dy, and d) are used to remove, the number of getting feature points is different, so it can be chosen according to the actual situation.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…The maximum and minimum distance method is taken as the matching criterion. Then 2-D matching points are obtained and further corrected by the literature [7]. Finally, the coordinate transforming algorithm above (Similar to the section 2.2) is used to estimate vehicle trajectory.…”

Section: Vehicle Motion Estimation Based On Feature Points Matchingmentioning

confidence: 99%

“…The correct matching points are obtained, and then the vehicle trajectory estimation is accomplished by the literature [7].…”

Section: Vehicle Motion Estimation Based On Feature Points Matchingmentioning

confidence: 99%

“…However, establishing the database also wasted time and required to make a regular update. In the literatures [7], [8], according to the homography matrix calculated by local feature points matching, vehicle trajectory was calculated to realize ego-localization. The method had the accurate positioning information in a short time, but it had low efficiency and was affected by the accumulated error to make the trajectory drifted over time.…”

Section: Introductionmentioning

confidence: 99%

“…Result of literature[7]: (a) different offset values; (b) eliminating effect using dx; (c) eliminating effect using d .…”

mentioning

confidence: 99%

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Vehicle Ego-Localization Based on the Fusion of Optical Flow and Feature Points Matching

et al. 2019

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To meet the requirement of vehicle real-time and precise ego-localization on the flat road of city, a vehicle ego-localization method based on the fusion of optical flow and feature points matching is proposed. A novel FAST algorithm with self-adaptive threshold is applied to detect feature points. Based on the assumption of flat plane, the improved Lucas-Kanade algorithm is carried out to track feature points, and then the custom LARSAE is used to amend vehicle offsets. Meanwhile, Hu moments are used as the feature descriptor to complete image matching, realizing vehicle motion estimation. These two methods are fused by the discrete kalman filter to update and optimize vehicle position. Experimental results show that the fusion algorithm overcomes the shortcomings of poor positioning accuracy of optical flow and the low processing speed of feature matching, and is able to provide more accurate real-time positioning output, having a certain robustness for circumstances such as illumination change and low pavement texture. INDEX TERMS Vehicle ego-localization, image matching, optical flow, optimized FAST, Kalman filter.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Section: Vehicle Motion Estimation Based On Feature Points Matchingmentioning

confidence: 99%

“…The correct matching points are obtained, and then the vehicle trajectory estimation is accomplished by the literature [7].…”

Section: Vehicle Motion Estimation Based On Feature Points Matchingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Result of literature[7]: (a) different offset values; (b) eliminating effect using dx; (c) eliminating effect using d .…”

mentioning

confidence: 99%

See 3 more Smart Citations

Vehicle Ego-Localization Based on the Fusion of Optical Flow and Feature Points Matching

et al. 2019

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Vehicle Ego-Localization Based on Streetscape Image Database Under Blind Area of Global Positioning System

Zhou

Zhao

Cheng

et al. 2018

J. Shanghai Jiaotong Univ. (Sci.)

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Safety assessment of vehicle behaviour based on the improved D–S evidence theory

Cheng

Zhou

Zhao

2020

IET Intelligent Trans Sys

Self Cite

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Vehicle dangerous behaviour warning plays an important role to improve road traffic safety and efficiency, so a safety assessment method of vehicle behaviour based on the improved Dempster–Shafer (D–S) evidence theory is proposed. Firstly, through analysis of vehicle collision accident mechanism, some factors closely related to vehicle safety are extracted. Also, multiple sensors are synthetically utilised to collect information, which realises the awareness of vehicle state, road attribute, driving environment etc. Then vehicle behaviour identification is accomplished according to the parameter information of the vehicle‐mounted sensors, as well as the related data of adjacent vehicles in vehicular ad hoc networks (VANET). Finally, a sequential type of weighted correction method based on evidence variance is used to integrate different levels of multi‐source heterogeneous information and to achieve safety assessment of vehicle behaviour. The experimental results show that the improved D–S evidence theory reduces the evidence conflict, increasing the accuracy and reliability of vehicle behaviour safety assessment. The study solves the fundamental core problem of active safety warning in VANET and provides a new means of traffic accident warning for the road traffic management department.

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Algorithm of Vehicle Trajectory Extraction using Fusion of Hu Moments and Grayscale Feature

Cited by 4 publications

References 4 publications

Vehicle Ego-Localization Based on the Fusion of Optical Flow and Feature Points Matching

Vehicle Ego-Localization Based on the Fusion of Optical Flow and Feature Points Matching

Vehicle Ego-Localization Based on Streetscape Image Database Under Blind Area of Global Positioning System

Safety assessment of vehicle behaviour based on the improved D–S evidence theory

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