In order to achieve a high precision in three-dimensional（3D） multi-camera measurement system, an efficient multi-cameracalibration method is proposed. A stitching method of large scalecalibration targets is deduced...In order to achieve a high precision in three-dimensional（3D） multi-camera measurement system, an efficient multi-cameracalibration method is proposed. A stitching method of large scalecalibration targets is deduced, and a fundamental of multi-cameracalibration based on the large scale calibration target is provided.To avoid the shortcomings of the method, the vector differencesof reprojection error with the presence of the constraint conditionof the constant rigid body transformation is modelled, and mini-mized by the Levenberg-Marquardt （LM） method. Results of thesimulation and observation data calibration experiment show thatthe accuracy of the system calibrated by the proposed methodreaches 2 mm when measuring distance section of 20 000 mmand scale section of 7 000 mm × 7 000 mm. Consequently, theproposed method of multi-camera calibration performs better thanthe fundamental in stability. This technique offers a more uniformerror distribution for measuring large scale space.展开更多
An approach based on fuzzy logic for matching both articulated and non-articulated objects across multiple non-overlapping field of views (FoVs) from multiple cameras is proposed. We call it fuzzy logic matching algor...An approach based on fuzzy logic for matching both articulated and non-articulated objects across multiple non-overlapping field of views (FoVs) from multiple cameras is proposed. We call it fuzzy logic matching algorithm (FLMA). The approach uses the information of object motion, shape and camera topology for matching objects across camera views. The motion and shape information of targets are obtained by tracking them using a combination of ConDensation and CAMShift tracking algorithms. The information of camera topology is obtained and used by calculating the projective transformation of each view with the common ground plane. The algorithm is suitable for tracking non-rigid objects with both linear and non-linear motion. We show videos of tracking objects across multiple cameras based on FLMA. From our experiments, the system is able to correctly match the targets across views with a high accuracy.展开更多
Multi-sensor vision system plays an important role in the 3D measurement of large objects.However,due to the widely distribution of sensors,the problem of lacking common fields of view（FOV） arises frequently,which m...Multi-sensor vision system plays an important role in the 3D measurement of large objects.However,due to the widely distribution of sensors,the problem of lacking common fields of view（FOV） arises frequently,which makes the global calibration of the vision system quite difficult.The primary existing solution relies on large-scale surveying equipments,which is ponderous and inconvenient for field calibrations.In this paper,a global calibration method of multi-sensor vision system is proposed and investigated.The proposed method utilizes pairs of skew laser lines,which are generated by a group of laser pointers,as the calibration objects.Each pair of skew laser lines provides a unique coordinate system in space which can be reconstructed in certain vision sensor＇s coordinates by using a planar pattern.Then the geometries of sensors are computed under rigid transformation constrains by taking coordinates of each skew lines pair as the intermediary.The method is applied on both visual cameras with synthetic data and a real two-camera vision system;results show the validity and good performance.The prime contribution of this paper is taking skew laser lines as the global calibration objects,which makes the method simple and flexible.The method need no expensive equipments and can be used in large-scale calibration.展开更多
It is difficult but important to get clear information from the low illumination images. In recent years the research of the low illumination image enhancement has become a hot topic in image processing and computer v...It is difficult but important to get clear information from the low illumination images. In recent years the research of the low illumination image enhancement has become a hot topic in image processing and computer vision. The Retinex algorithm is one of the most popular methods in the field and uniform illumination is necessary to enhance low illumination image quality by using this algorithm. However, for the different areas of an image with contrast brightness differences, the illumination image is not smooth and causes halo artifacts so that it cannot retain the detail information of the original images. To solve the problem, we generalize the multi-scale Retinex algorithm and propose a new enhancement method for the low illumination images based on the microarray camera. The proposed method can well make up for the deficiency of imbalanced illumination and significantly inhibit the halo artifacts as well. Experimental results show that the proposed method can get better image enhancement effect compared to the multi-scale Retinex algorithm of a single image enhancement. Advantages of the method also include that it can significantly inhibit the halo artifacts and thus retain the details of the original images, it can improve the brightness and contrast of the image as well. The newly developed method in this paper has application potential to the images captured by pad and cell phone in the low illumination environment.展开更多
为减少因船舶偏离航道而造成的搁浅、碰撞航标或桥墩等水上交通事故,提出了一种基于多目相机自动识别航道的桥区航行异常船舶预警方法。基于YOLOv5(You Only Look Once version 5)目标检测算法,联动变、定焦相机识别并定位航标和船舶,...为减少因船舶偏离航道而造成的搁浅、碰撞航标或桥墩等水上交通事故,提出了一种基于多目相机自动识别航道的桥区航行异常船舶预警方法。基于YOLOv5(You Only Look Once version 5)目标检测算法,联动变、定焦相机识别并定位航标和船舶,跟踪并记录船舶航迹点,计算船舶的速度和航向并推算船位。提出了一种基于视频船舶航迹点的密度聚类识别航道两侧航标的方法,实现航道自适应可视化。基于船位推算识别并预警航行状态异常的船舶。实验结果表明:航标、船舶的检测正确率分别达84.8%、90.3%,相较单一相机检测模型,正确率分别提高了32.1%、5.5%;能够自适应可视化航道并识别、预警航行异常船舶。展开更多
为提高无人车障碍物检测跟踪的精度和稳定性,首先针对YOLO v5(You only look once version 5,YOLO v5)网络存在的语义信息和候选框信息丢失的问题,引入深度可分离空洞空间金字塔结构与目标框加权融合算法完成对网络的优化;其次针对单阶...为提高无人车障碍物检测跟踪的精度和稳定性,首先针对YOLO v5(You only look once version 5,YOLO v5)网络存在的语义信息和候选框信息丢失的问题,引入深度可分离空洞空间金字塔结构与目标框加权融合算法完成对网络的优化;其次针对单阶段障碍物点云聚类精度低的问题,设计一种考虑点云距离与外轮廓连续性的两阶段障碍物点云聚类方法并完成三维包围盒的建立;最后将注意力机制引入MobileNet使网络更加聚焦于目标对象特有的视觉特征,并综合利用视觉特征和三维点云信息共同构建关联性度量指标,提高匹配精度。利用KITTI数据集对构建的障碍物目标检测、跟踪与测速算法进行仿真测试,并搭建实车平台进行真实环境试验,验证所提算法的有效性和真实环境可迁移性。展开更多
基金supported by the National Natural Science Foundation of China(61473100)
文摘In order to achieve a high precision in three-dimensional（3D） multi-camera measurement system, an efficient multi-cameracalibration method is proposed. A stitching method of large scalecalibration targets is deduced, and a fundamental of multi-cameracalibration based on the large scale calibration target is provided.To avoid the shortcomings of the method, the vector differencesof reprojection error with the presence of the constraint conditionof the constant rigid body transformation is modelled, and mini-mized by the Levenberg-Marquardt （LM） method. Results of thesimulation and observation data calibration experiment show thatthe accuracy of the system calibrated by the proposed methodreaches 2 mm when measuring distance section of 20 000 mmand scale section of 7 000 mm × 7 000 mm. Consequently, theproposed method of multi-camera calibration performs better thanthe fundamental in stability. This technique offers a more uniformerror distribution for measuring large scale space.
文摘An approach based on fuzzy logic for matching both articulated and non-articulated objects across multiple non-overlapping field of views (FoVs) from multiple cameras is proposed. We call it fuzzy logic matching algorithm (FLMA). The approach uses the information of object motion, shape and camera topology for matching objects across camera views. The motion and shape information of targets are obtained by tracking them using a combination of ConDensation and CAMShift tracking algorithms. The information of camera topology is obtained and used by calculating the projective transformation of each view with the common ground plane. The algorithm is suitable for tracking non-rigid objects with both linear and non-linear motion. We show videos of tracking objects across multiple cameras based on FLMA. From our experiments, the system is able to correctly match the targets across views with a high accuracy.
基金supported by National Natural Science Foundation of China (Grant No. 60804060)Research Fund for the Doctoral Program of Higher Education of China (Grant No. 200800061003)
文摘Multi-sensor vision system plays an important role in the 3D measurement of large objects.However,due to the widely distribution of sensors,the problem of lacking common fields of view（FOV） arises frequently,which makes the global calibration of the vision system quite difficult.The primary existing solution relies on large-scale surveying equipments,which is ponderous and inconvenient for field calibrations.In this paper,a global calibration method of multi-sensor vision system is proposed and investigated.The proposed method utilizes pairs of skew laser lines,which are generated by a group of laser pointers,as the calibration objects.Each pair of skew laser lines provides a unique coordinate system in space which can be reconstructed in certain vision sensor＇s coordinates by using a planar pattern.Then the geometries of sensors are computed under rigid transformation constrains by taking coordinates of each skew lines pair as the intermediary.The method is applied on both visual cameras with synthetic data and a real two-camera vision system;results show the validity and good performance.The prime contribution of this paper is taking skew laser lines as the global calibration objects,which makes the method simple and flexible.The method need no expensive equipments and can be used in large-scale calibration.
基金Supported by National Science and Technology Major Project(2014ZX02502003The National Natural Science Foundation of China(61170327)
文摘It is difficult but important to get clear information from the low illumination images. In recent years the research of the low illumination image enhancement has become a hot topic in image processing and computer vision. The Retinex algorithm is one of the most popular methods in the field and uniform illumination is necessary to enhance low illumination image quality by using this algorithm. However, for the different areas of an image with contrast brightness differences, the illumination image is not smooth and causes halo artifacts so that it cannot retain the detail information of the original images. To solve the problem, we generalize the multi-scale Retinex algorithm and propose a new enhancement method for the low illumination images based on the microarray camera. The proposed method can well make up for the deficiency of imbalanced illumination and significantly inhibit the halo artifacts as well. Experimental results show that the proposed method can get better image enhancement effect compared to the multi-scale Retinex algorithm of a single image enhancement. Advantages of the method also include that it can significantly inhibit the halo artifacts and thus retain the details of the original images, it can improve the brightness and contrast of the image as well. The newly developed method in this paper has application potential to the images captured by pad and cell phone in the low illumination environment.
文摘为减少因船舶偏离航道而造成的搁浅、碰撞航标或桥墩等水上交通事故,提出了一种基于多目相机自动识别航道的桥区航行异常船舶预警方法。基于YOLOv5(You Only Look Once version 5)目标检测算法,联动变、定焦相机识别并定位航标和船舶,跟踪并记录船舶航迹点,计算船舶的速度和航向并推算船位。提出了一种基于视频船舶航迹点的密度聚类识别航道两侧航标的方法,实现航道自适应可视化。基于船位推算识别并预警航行状态异常的船舶。实验结果表明:航标、船舶的检测正确率分别达84.8%、90.3%,相较单一相机检测模型,正确率分别提高了32.1%、5.5%;能够自适应可视化航道并识别、预警航行异常船舶。
文摘为提高无人车障碍物检测跟踪的精度和稳定性,首先针对YOLO v5(You only look once version 5,YOLO v5)网络存在的语义信息和候选框信息丢失的问题,引入深度可分离空洞空间金字塔结构与目标框加权融合算法完成对网络的优化;其次针对单阶段障碍物点云聚类精度低的问题,设计一种考虑点云距离与外轮廓连续性的两阶段障碍物点云聚类方法并完成三维包围盒的建立;最后将注意力机制引入MobileNet使网络更加聚焦于目标对象特有的视觉特征,并综合利用视觉特征和三维点云信息共同构建关联性度量指标,提高匹配精度。利用KITTI数据集对构建的障碍物目标检测、跟踪与测速算法进行仿真测试,并搭建实车平台进行真实环境试验,验证所提算法的有效性和真实环境可迁移性。
