home video analysis, video to photo, video printing, camera motion analysis, intelligent keyframe extraction, video panorama, superresolution, user intent, motion mining More and more home videos have been generated with the ever growing popularity of digital cameras and camcorders. In many cases of home video, a photo, whether capturing a moment or a scene within the video, provides a compleme...

The VideoCLEF track, introduced in 2008, aims to develop and evaluate tasks related to analysis of and access to multilingual multimedia content. In its first year, VideoCLEF piloted the Vid2RSS task, whose main subtask was the classification of dual language video (Dutchlanguage television content featuring English-speaking experts and studio guests). The task offered two additional discretion...

The rate of increase in multimedia data required the need for an improved bandwidth utilization and storage capacity. However, low-motion videos come with a large number feature-related frames due to its static background. These redundant result difficulty terms video streaming, retrieval, transmission. In other improve user experience, summarization technologies were proposed. techniques prese...

We present a novel tightly-coupled LiDAR-inertial odometry and mapping scheme for both solid-state mechanical LiDARs. As frontend, feature-based lightweight LiDAR provides fast motion estimates adaptive keyframe selection. backend, hierarchical keyframe-based sliding window optimization is performed through marginalization directly fusing IMU measurements. For the Livox Horizon, newly released ...

Robots should be able to learn skills from humans not only through kinesthetic teaching, but also by recognizing intentions and abstract concepts in human behavior. This work presents a method that enables robots to learn humandemonstrated concepts on an abstract, first-order logic based representational layer, while addressing the problems of keyframe extraction, concept learning, symbolic gro...

Abstract Combining convolutional neural networks (CNNs) and recurrent (RNNs) produces a powerful architecture for video classification problems as spatial–temporal information can be processed simultaneously effectively. Using transfer learning, this paper presents comparative study to investigate how temporal utilized improve the performance of when CNNs RNNs are combined in various architectu...

The large amounts of surveillance video data are recorded, containing many redundant frames, which makes browsing and retrieval difficult, thus increasing bandwidth utilization, storage capacity, time consumed. To ensure the reduction in utilization capacity to barest minimum, keyframe extraction strategies have been developed. These implemented extract unique keyframes whilst removing redundan...

Video segmentation and keyframe extraction are the basis of Content-based Video Retrieval (CBVR), in which keyframe selection is at the very core of CBVR. At shot level, key-frame extraction provides sufficient indexing and browsing of large video databases. In this paper, we proposed two improved approaches of key-frame extraction for video summarization. In our first synthesis method based on...