针对工程车辆行驶速度低、滑转率高的特点,提出了一种基于双目序列图像的检测方法,以便快速检测工程车辆的相对位置与实际行驶速度。将双目摄像机安装在车辆上,连续采集周围环境的序列图像;利用SURF(speeded up robust features)特征对...针对工程车辆行驶速度低、滑转率高的特点,提出了一种基于双目序列图像的检测方法,以便快速检测工程车辆的相对位置与实际行驶速度。将双目摄像机安装在车辆上,连续采集周围环境的序列图像;利用SURF(speeded up robust features)特征对已采集到的各帧双目图像进行立体匹配,计算出环境特征点到摄像机坐标系原点的距离,从而实现车辆的相对定位;再对相邻两帧图像进行特征跟踪匹配,根据不同景深将匹配特征点对划分为远距点对和近距点对,分别利用远距点对和近距点对估算车辆运动过程中坐标系的旋转矩阵和平移矢量,并利用Levenberg-Marquardt法进行优化求解;最后根据优化后的旋转矩阵和平移矢量计算出车辆的行驶速度。户外模拟试验结果表明了方法的有效性和可行性。展开更多
Research in the field ofmedical image is an important part of themedical robot to operate human organs.Amedical robot is the intersection ofmulti-disciplinary research fields,in whichmedical image is an important dire...Research in the field ofmedical image is an important part of themedical robot to operate human organs.Amedical robot is the intersection ofmulti-disciplinary research fields,in whichmedical image is an important direction and has achieved fruitful results.In this paper,amethodof soft tissue surface feature tracking basedonadepthmatching network is proposed.This method is described based on the triangular matching algorithm.First,we construct a self-made sample set for training the depth matching network from the first N frames of speckle matching data obtained by the triangle matching algorithm.The depth matching network is pre-trained on the ORL face data set and then trained on the self-made training set.After the training,the speckle matching is carried out in the subsequent frames to obtain the speckle matching matrix between the subsequent frames and the first frame.From this matrix,the inter-frame feature matching results can be obtained.In this way,the inter-frame speckle tracking is completed.On this basis,the results of this method are compared with the matching results based on the convolutional neural network.The experimental results show that the proposed method has higher matching accuracy.In particular,the accuracy of the MNIST handwritten data set has reached more than 90%.展开更多
为了确定车辆在行驶过程中的相对位置与速度,提出一种基于双目序列图像的实时测距定位及自车速度估计方法。该方法利用车载双目视觉传感器采集周围环境的序列图像,并对同一时刻的左右图像进行基于SURF(speeded up robust features)特征...为了确定车辆在行驶过程中的相对位置与速度,提出一种基于双目序列图像的实时测距定位及自车速度估计方法。该方法利用车载双目视觉传感器采集周围环境的序列图像,并对同一时刻的左右图像进行基于SURF(speeded up robust features)特征的立体匹配,以获取环境特征点的景深,实现车辆测距定位;同时又对相邻两帧图像进行基于SURF特征的跟踪匹配,并通过对应匹配点在相邻两帧摄像机坐标系下的三维坐标,计算出摄像机坐标系在车辆运动前后的变换参数,根据变换参数估算出车辆的行驶速度。模拟实验表明,该方法具有良好的可行性,速度计算结果比较稳定,平均误差均在6%以内。展开更多
Long duration visual tracking of targets is quite challenging for computer vision, because the environments may be cluttered and distracting. Illumination variations and partial occlusions are two main difficulties in...Long duration visual tracking of targets is quite challenging for computer vision, because the environments may be cluttered and distracting. Illumination variations and partial occlusions are two main difficulties in real world visual tracking. Existing methods based on hostile appearance information cannot solve these problems effectively. This paper proposes a feature-based dynamic tracking approach that can track objects with partial occlusions and varying illumination. The method represents the tracked object by an invariant feature model. During the tracking, a new pyramid matching algorithm was used to match the object template with the observations to determine the observation likelihood. This matching is quite efficient in calculation and the spatial constraints among these features are also embedded. Instead of complicated optimization methods, the whole model is incorporated into a Bayesian filtering framework. The experiments on real world sequences demonstrate that the method can track objects accurately and robustly even with illumination variations and partial occlusions.展开更多
This paper proposes a detecting and tracking scheme for automatic checking attendance of traffic controllers in level crossing by recognizing their warning waistcoats. Considering of the actual requirement of rapidity...This paper proposes a detecting and tracking scheme for automatic checking attendance of traffic controllers in level crossing by recognizing their warning waistcoats. Considering of the actual requirement of rapidity and validity, this paper employs techniques of motion detection, color segmentation and feature matching to deal with the challenging problems of illumination varying, light reflection and disturbance. Therefore, the task of distinguishing the target from candidates can be fulfilled accurately. Once a target being detected, the established color models are modified through learning color of the detected target, and then Cam-shift algorithm is employed to track this target smoothly. The experiments in real scenes demonstrate that this method has a great capability to detect and track traffic controllers in complex level crossing environment accurately, and the comparisons further demonstrate the validity of the proposed method.展开更多
文摘针对工程车辆行驶速度低、滑转率高的特点,提出了一种基于双目序列图像的检测方法,以便快速检测工程车辆的相对位置与实际行驶速度。将双目摄像机安装在车辆上,连续采集周围环境的序列图像;利用SURF(speeded up robust features)特征对已采集到的各帧双目图像进行立体匹配,计算出环境特征点到摄像机坐标系原点的距离,从而实现车辆的相对定位;再对相邻两帧图像进行特征跟踪匹配,根据不同景深将匹配特征点对划分为远距点对和近距点对,分别利用远距点对和近距点对估算车辆运动过程中坐标系的旋转矩阵和平移矢量,并利用Levenberg-Marquardt法进行优化求解;最后根据优化后的旋转矩阵和平移矢量计算出车辆的行驶速度。户外模拟试验结果表明了方法的有效性和可行性。
基金supported by the Sichuan Science and Technology Program (Grant:2021YFQ0003,Acquired by Wenfeng Zheng).
文摘Research in the field ofmedical image is an important part of themedical robot to operate human organs.Amedical robot is the intersection ofmulti-disciplinary research fields,in whichmedical image is an important direction and has achieved fruitful results.In this paper,amethodof soft tissue surface feature tracking basedonadepthmatching network is proposed.This method is described based on the triangular matching algorithm.First,we construct a self-made sample set for training the depth matching network from the first N frames of speckle matching data obtained by the triangle matching algorithm.The depth matching network is pre-trained on the ORL face data set and then trained on the self-made training set.After the training,the speckle matching is carried out in the subsequent frames to obtain the speckle matching matrix between the subsequent frames and the first frame.From this matrix,the inter-frame feature matching results can be obtained.In this way,the inter-frame speckle tracking is completed.On this basis,the results of this method are compared with the matching results based on the convolutional neural network.The experimental results show that the proposed method has higher matching accuracy.In particular,the accuracy of the MNIST handwritten data set has reached more than 90%.
文摘为了确定车辆在行驶过程中的相对位置与速度,提出一种基于双目序列图像的实时测距定位及自车速度估计方法。该方法利用车载双目视觉传感器采集周围环境的序列图像,并对同一时刻的左右图像进行基于SURF(speeded up robust features)特征的立体匹配,以获取环境特征点的景深,实现车辆测距定位;同时又对相邻两帧图像进行基于SURF特征的跟踪匹配,并通过对应匹配点在相邻两帧摄像机坐标系下的三维坐标,计算出摄像机坐标系在车辆运动前后的变换参数,根据变换参数估算出车辆的行驶速度。模拟实验表明,该方法具有良好的可行性,速度计算结果比较稳定,平均误差均在6%以内。
文摘Long duration visual tracking of targets is quite challenging for computer vision, because the environments may be cluttered and distracting. Illumination variations and partial occlusions are two main difficulties in real world visual tracking. Existing methods based on hostile appearance information cannot solve these problems effectively. This paper proposes a feature-based dynamic tracking approach that can track objects with partial occlusions and varying illumination. The method represents the tracked object by an invariant feature model. During the tracking, a new pyramid matching algorithm was used to match the object template with the observations to determine the observation likelihood. This matching is quite efficient in calculation and the spatial constraints among these features are also embedded. Instead of complicated optimization methods, the whole model is incorporated into a Bayesian filtering framework. The experiments on real world sequences demonstrate that the method can track objects accurately and robustly even with illumination variations and partial occlusions.
基金the National Natural Science Foundation of China(No.51175459)
文摘This paper proposes a detecting and tracking scheme for automatic checking attendance of traffic controllers in level crossing by recognizing their warning waistcoats. Considering of the actual requirement of rapidity and validity, this paper employs techniques of motion detection, color segmentation and feature matching to deal with the challenging problems of illumination varying, light reflection and disturbance. Therefore, the task of distinguishing the target from candidates can be fulfilled accurately. Once a target being detected, the established color models are modified through learning color of the detected target, and then Cam-shift algorithm is employed to track this target smoothly. The experiments in real scenes demonstrate that this method has a great capability to detect and track traffic controllers in complex level crossing environment accurately, and the comparisons further demonstrate the validity of the proposed method.
