Motion-Based Selection of Relevant Video Segments for Video Summarization

Based on compressed video segment detection, the decompression step can be omitted, features can be directly extracted from the original video data stream, and the detection rate can be accelerated. This paper analyzes the characteristics of video data, video segmentation and key frame selection. Based on the analysis of the key technologies of video retrieval technology, the compressed video stream extraction method based on video segmentation and key frame is introduced. A method of extracting key frames from MPEG compressed video based on DC coefficients and motion vectors is proposed. Experiments show that the proposed method can reduce the computational burden, and can better represent the video content.

The video segmentation is the basis of video analysis and digging, the results directly affect the accuracy of follow-up processing. For the video segmentation, the predecessors have done a IoT of research. We have achieved very good results in regard to the mutant lens, but there is no good way for the gradient. Because football is a popular sports, it has a very practical significance for the process. But since the football video screen changes rapidly, which increases the difficulty of segmentation. This paper first introduces five commonly used methods of shot segmentation, and then introduces three methods for soccer video segmentation, and finally gives a combination of methods used in soccer video segmentation. Hope that this article allows you to more quickly understand the methods of the shot segmentation about soccer videoes.

In this paper, we describe a method for video summarization that operates on a video segment level. We formulate this problem as the one of automatic video segment selection based on a learning process that employs salient video segment paradigms. We design an hierarchical learning scheme that consists of two steps. At the first step, an unsupervised process is performed in order to determine salient video segment types. The second step is a supervised learning process that is performed for each of the salient video segment type independently. For the latter case, since only salient training examples are available, the problem is stated as an one-class classification problem. In order to take into account subclass information that may appear in the video segment types, we introduce a novel formulation of the Support Vector Data Description method that exploits subclass information in its optimization process. We evaluate the proposed approach in three Hollywood movies, where the performance of the proposed Subclass SVDD (SSVDD) algorithm is compared with that of related methods. Experimental results show that the adoption of both hierarchical learning and the proposed SSVDD method contribute to the final classification performance.

Flow-guided feature propagation with occlusion aware detail enhancement for hand segmentation in egocentric videos

Triple attention network for video segmentation

Salient object segmentation in videos is generally broken up in a video segmentation part and a saliency assignment part. Recently, object proposals, which are used to segment the image, have had significant impact on many computer vision applications, including image segmentation, object detection, and recently saliency detection in still images. However, their usage has not yet been evaluated for salient object segmentation in videos. Therefore, in this paper, we investigate the application of object proposals to salient object segmentation in videos. In addition, we propose a new motion feature derived from the optical flow structure tensor for video saliency detection. Experiments on two standard benchmark datasets for video saliency show that the proposed motion feature improves saliency estimation results, and that object proposals are an efficient method for salient object segmentation. Results on the challenging SegTrack v2 and Fukuchi benchmark data sets show that we significantly outperform the state-of-the-art.

Video Annotation by Motion Interpretation Using Optical Flow Streams

To improve the robustness and efficiency of RGBD video segmentation, we propose a novel video segmentation method combining multi-scale plane extraction and hierarchical graph-based video segmentation. Firstly, to reduce depth data noise, we extract plane structures of 3D RGBD point clouds in three levels including voxel, pixel and neighborhood with geometry and color features. To solve uneven distribution of depth data and object occlusion problem, we further propose multi-scale voxel based plane fusion algorithm and use amodal completion strategy to improve plane extraction performance. Then hierarchical graph-based RGBD video segmentation is used to segment the rest of the non-plane pixels. Finally, we fuse above plane extraction and video segmentation results to get final RGBD video scene segmentation results. The qualitative and quantitative results of plane extraction and RGBD scene video segmentation show the effectiveness of proposed methods.

In the new MPEG-4 video coding standard, the semi-automatic video segmentation plays a key role insupporting object-oriented coding and enabling content-based functionalities. A novel hierarchy optical flow basedsemi-automatic video segmentation method is presented in this paper. The proposed segmentation method containsspatial and temporal segmentation. For the spatial segmentation, a point-based graphic user interface (PBGUI) ispresented, with which the user can input easily, and then active contour model and tracking bug algorithm are applied toprecisely define the video object of interest to be segmented. With the result of spatial segmentation, the temporalsegmentation involves non-rigid object contour tracking and rigid object whole-tracking by hierarchy optical flowalgorithm based on the Lucas-Kanade algorithm. And the tracking point selection algorithm is proposed to greatlyimprove the tracking performance in the rigid object whole-tracking. The experimental results show that the proposedalgorithm can precisely segment video objects from video streams.Keywords: MPEG-4, optical flow, video segmentation, object-oriented coding

The amount of user created contents has been increasing rapidly and is associated with a serious copyright problem. Automatic logo detection and recognition in videos is a natural and efficient way of overcoming the copyright problem. However, logos have varying characteristics, which make logo detection and recognition very difficult. Moreover, logo transitions between two different logos exist in one video comprising several video contents. This disrupts the automatic logo detection and recognition. Therefore, in order to improve logo detection, it is necessary to take into account the logo transitions explicitly. This paper proposes an accurate logo transition detection method for recognizing logos in digital video contents. The proposed method accurately segments a video according to logo and efficiently recognizes various types of logos. The experimental results demonstrate the effectiveness of the proposed method for logo detection and video segmentation according to logo.

In this paper, we propose a general Eulerian framework for region-based active contours named DREAM2S. We introduce a general criterion including both region-based and boundary-based terms where the information on a region is named “descriptor”. The originality of this work is twofold. Firstly we propose to use shape optimization principles to compute the evolution equation of the active contour that will make it evolve as fast as possible towards a minimum of the criterion. Secondly, we take into account the variation of the descriptors during the propagation of the curve. Indeed, a descriptor is generally globally attached to the region and thus “region-dependent”. This case arises for example if the mean or the variance of a region are chosen as descriptors. We show that the dependence of the descriptors with the region induces additional terms in the evolution equation of the active contour that have never been previously computed. DREAM2S gives an easy way to take such a dependence into account and to compute the resulting additional terms. Experimental results point out the importance of the additional terms to reach a true minimum of the criterion and so to obtain accurate results. The covariance matrix determinant appears to be a very relevant tool for homogeneous color regions segmentation. As an example, it has been successfully applied to face detection in real video sequences.KeywordsEvolution EquationVideo SequenceAdditional TermActive ContourHomogeneous RegionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Video data provides a rich source of information that is available to us today in large quantities e.g. from on-line resources. Tasks like segmentation benefit greatly from the analysis of spatio-temporal motion patterns in videos and recent advances in video segmentation has shown great progress in exploiting these addition cues. However, observing a single video is often not enough to predict meaningful segmentations and inference across videos becomes necessary in order to predict segmentations that are consistent with objects classes. Therefore the task of video co-segmentation is being proposed, that aims at inferring segmentation from multiple videos. But current approaches are limited to only considering binary foreground/background segmentation and multiple videos of the same object. This is a clear mismatch to the challenges that we are facing with videos from online resources or consumer videos. We propose to study multi-class video co-segmentation where the number of object classes is unknown as well as the number of instances in each frame and video. We achieve this by formulating a non-parametric Bayesian model across videos sequences that is based on a new videos segmentation prior as well as a global appearance model that links segments of the same class. We present the first multi-class video co-segmentation evaluation. We show that our method is applicable to real video data from online resources and outperforms state-of-the-art video segmentation and image co-segmentation baselines.

A Video Segmentation and Key Frame Extraction Method for Compressed Video Stream

Se analizan las diferentes maneras en el que el documentalista realiza la segmentación del vídeo para así identificar la unidad discursiva mínima de análisis en los departamentos de documentación de las cadenas televisiva. De forma paralela, y atendiendo al cambio de paradigma digital en la producción en televisión, se analizan las principales fórmulas de segmentación automática de vídeo desarrolladas por medio de la inteligencia artificial. Una vez determinada ambas realidades se intenta implementar el mejor método de segmentación entre lo humano y lo automático, para que tenga su utilidad en los sistemas de información documental de las televisiones. Para ello se ofrecen las principales líneas de trabajo en segmentación semántica de vídeo.

Validity and reliability of naturalistic driving scene categorization Judgments from crowdsourcing