Due to the exponential growth of video data,aided by rapid advancements in multimedia technologies.It became difficult for the user to obtain information from a large video series.The process of providing an abstract ...Due to the exponential growth of video data,aided by rapid advancements in multimedia technologies.It became difficult for the user to obtain information from a large video series.The process of providing an abstract of the entire video that includes the most representative frames is known as static video summarization.This method resulted in rapid exploration,indexing,and retrieval of massive video libraries.We propose a framework for static video summary based on a Binary Robust Invariant Scalable Keypoint(BRISK)and bisecting K-means clustering algorithm.The current method effectively recognizes relevant frames using BRISK by extracting keypoints and the descriptors from video sequences.The video frames’BRISK features are clustered using a bisecting K-means,and the keyframe is determined by selecting the frame that is most near the cluster center.Without applying any clustering parameters,the appropriate clusters number is determined using the silhouette coefficient.Experiments were carried out on a publicly available open video project(OVP)dataset that contained videos of different genres.The proposed method’s effectiveness is compared to existing methods using a variety of evaluation metrics,and the proposed method achieves a trade-off between computational cost and quality.展开更多
同步定位与地图构建(Simultaneous Localization and Mapping, SLAM)是机器人自主导航的关键技术。针对目前激光SLAM算法用于地面机器人位姿估计时出现的特征匹配可靠性不足、高程误差漂移等问题,提出一种基于地面约束的改进A-LOAM算法...同步定位与地图构建(Simultaneous Localization and Mapping, SLAM)是机器人自主导航的关键技术。针对目前激光SLAM算法用于地面机器人位姿估计时出现的特征匹配可靠性不足、高程误差漂移等问题,提出一种基于地面约束的改进A-LOAM算法。算法首先通过分割地面点优化特征提取的过程从而获取更加可靠的特征点用于帧间匹配,进而在帧图匹配过程提取关键帧构建局部地图并加入地面约束减小高程误差。上述算法还加入了回环检测模块进一步抑制误差累积。实验证明,提出的算法轨迹估计精度高于A-LOAM,且加入的地面约束有效减小了估计误差。展开更多
基金The authors would like to thank Research Supporting Project Number(RSP2024R444)King Saud University,Riyadh,Saudi Arabia.
文摘Due to the exponential growth of video data,aided by rapid advancements in multimedia technologies.It became difficult for the user to obtain information from a large video series.The process of providing an abstract of the entire video that includes the most representative frames is known as static video summarization.This method resulted in rapid exploration,indexing,and retrieval of massive video libraries.We propose a framework for static video summary based on a Binary Robust Invariant Scalable Keypoint(BRISK)and bisecting K-means clustering algorithm.The current method effectively recognizes relevant frames using BRISK by extracting keypoints and the descriptors from video sequences.The video frames’BRISK features are clustered using a bisecting K-means,and the keyframe is determined by selecting the frame that is most near the cluster center.Without applying any clustering parameters,the appropriate clusters number is determined using the silhouette coefficient.Experiments were carried out on a publicly available open video project(OVP)dataset that contained videos of different genres.The proposed method’s effectiveness is compared to existing methods using a variety of evaluation metrics,and the proposed method achieves a trade-off between computational cost and quality.
文摘同步定位与地图构建(Simultaneous Localization and Mapping, SLAM)是机器人自主导航的关键技术。针对目前激光SLAM算法用于地面机器人位姿估计时出现的特征匹配可靠性不足、高程误差漂移等问题,提出一种基于地面约束的改进A-LOAM算法。算法首先通过分割地面点优化特征提取的过程从而获取更加可靠的特征点用于帧间匹配,进而在帧图匹配过程提取关键帧构建局部地图并加入地面约束减小高程误差。上述算法还加入了回环检测模块进一步抑制误差累积。实验证明,提出的算法轨迹估计精度高于A-LOAM,且加入的地面约束有效减小了估计误差。
