Segmentation of moving objects in a video sequence is a basic task for application of computer vision. However, shadows extracted along with the objects can result in large errors in object localization and recognitio...Segmentation of moving objects in a video sequence is a basic task for application of computer vision. However, shadows extracted along with the objects can result in large errors in object localization and recognition. In this paper, we propose a method of moving shadow detection based on edge information, which can effectively detect the cast shadow of a moving vehicle in a traffic scene. Having confirmed shadows existing in a figure, we execute the shadow removal algorithm proposed in this paper to segment the shadow from the foreground. The shadow eliminating algorithm removes the boundary of the cast shadow and preserves object edges firstly; secondly, it reconstructs coarse object shapes based on the edge information of objects; and finally, it extracts the cast shadow by subtracting the moving object from the change detection mask and performs further processing. The proposed method has been further tested on images taken under different shadow orientations, vehicle colors and vehicle sizes, and the results have revealed that shadows can be successfully eliminated and thus good video segmentation can be obtained.展开更多
针对传统的SLAM算法在动态场景下易出现动态物体检测不完整以及难以准确判断潜在动态物体的运动状态等问题,提出一种动态场景下基于注意力机制与几何约束的VSLAM算法(VSLAM algorithm based on Attention mechanism and Geometric const...针对传统的SLAM算法在动态场景下易出现动态物体检测不完整以及难以准确判断潜在动态物体的运动状态等问题,提出一种动态场景下基于注意力机制与几何约束的VSLAM算法(VSLAM algorithm based on Attention mechanism and Geometric constraints in Dynamic scenes,AGD-SLAM)。该算法通过设计一种聚合注意力模块,引导模型关注图像中的漏检测区域,同时引入自适应空间特征融合网络ASFF,增强特征提取能力,避免漏检测发生。为减少动态物体对SLAM系统定位精度的干扰,通过基于双重静态点约束的位姿优化估计潜在动态物体运动状态,最终使用全部静态点进行位姿估计和地图构建。在公开TUM数据集进行测试,测试结果表明所提算法的绝对轨迹误差与DynaSLAM相比降低了10.98%,表现出良好的构图能力。展开更多
Simultaneous localisation and mapping(SLAM)are the basis for many robotic applications.As the front end of SLAM,visual odometry is mainly used to estimate camera pose.In dynamic scenes,classical methods are deteriorat...Simultaneous localisation and mapping(SLAM)are the basis for many robotic applications.As the front end of SLAM,visual odometry is mainly used to estimate camera pose.In dynamic scenes,classical methods are deteriorated by dynamic objects and cannot achieve satisfactory results.In order to improve the robustness of visual odometry in dynamic scenes,this paper proposed a dynamic region detection method based on RGBD images.Firstly,all feature points on the RGB image are classified as dynamic and static using a triangle constraint and the epipolar geometric constraint successively.Meanwhile,the depth image is clustered using the K-Means method.The classified feature points are mapped to the clustered depth image,and a dynamic or static label is assigned to each cluster according to the number of dynamic feature points.Subsequently,a dynamic region mask for the RGB image is generated based on the dynamic clusters in the depth image,and the feature points covered by the mask are all removed.The remaining static feature points are applied to estimate the camera pose.Finally,some experimental results are provided to demonstrate the feasibility and performance.展开更多
基金The work was supported by the National Natural Science Foundation of PRC (No.60574033)the National Key Fundamental Research & Development Programs(973)of PRC (No.2001CB309403)
文摘Segmentation of moving objects in a video sequence is a basic task for application of computer vision. However, shadows extracted along with the objects can result in large errors in object localization and recognition. In this paper, we propose a method of moving shadow detection based on edge information, which can effectively detect the cast shadow of a moving vehicle in a traffic scene. Having confirmed shadows existing in a figure, we execute the shadow removal algorithm proposed in this paper to segment the shadow from the foreground. The shadow eliminating algorithm removes the boundary of the cast shadow and preserves object edges firstly; secondly, it reconstructs coarse object shapes based on the edge information of objects; and finally, it extracts the cast shadow by subtracting the moving object from the change detection mask and performs further processing. The proposed method has been further tested on images taken under different shadow orientations, vehicle colors and vehicle sizes, and the results have revealed that shadows can be successfully eliminated and thus good video segmentation can be obtained.
文摘针对传统的SLAM算法在动态场景下易出现动态物体检测不完整以及难以准确判断潜在动态物体的运动状态等问题,提出一种动态场景下基于注意力机制与几何约束的VSLAM算法(VSLAM algorithm based on Attention mechanism and Geometric constraints in Dynamic scenes,AGD-SLAM)。该算法通过设计一种聚合注意力模块,引导模型关注图像中的漏检测区域,同时引入自适应空间特征融合网络ASFF,增强特征提取能力,避免漏检测发生。为减少动态物体对SLAM系统定位精度的干扰,通过基于双重静态点约束的位姿优化估计潜在动态物体运动状态,最终使用全部静态点进行位姿估计和地图构建。在公开TUM数据集进行测试,测试结果表明所提算法的绝对轨迹误差与DynaSLAM相比降低了10.98%,表现出良好的构图能力。
基金supported in part by the National Natural Science Foundation of China(Grant No.U1913201,U22B2041)Natural Science Foundation of Liaoning Province(Grant No.2019-ZD-0169).
文摘Simultaneous localisation and mapping(SLAM)are the basis for many robotic applications.As the front end of SLAM,visual odometry is mainly used to estimate camera pose.In dynamic scenes,classical methods are deteriorated by dynamic objects and cannot achieve satisfactory results.In order to improve the robustness of visual odometry in dynamic scenes,this paper proposed a dynamic region detection method based on RGBD images.Firstly,all feature points on the RGB image are classified as dynamic and static using a triangle constraint and the epipolar geometric constraint successively.Meanwhile,the depth image is clustered using the K-Means method.The classified feature points are mapped to the clustered depth image,and a dynamic or static label is assigned to each cluster according to the number of dynamic feature points.Subsequently,a dynamic region mask for the RGB image is generated based on the dynamic clusters in the depth image,and the feature points covered by the mask are all removed.The remaining static feature points are applied to estimate the camera pose.Finally,some experimental results are provided to demonstrate the feasibility and performance.
