A comprehensive survey of multi-view video summarization

[EN] There has been an exponential growth in the amount of visual data on a daily basis acquired from single or multi-view surveillance camera networks. This massive amount of data requires efficient mechanisms such as video summarization to ensure that only significant data are reported and the redundancy is reduced. Multi-view video summarization (MVS) is a less redundant and more concise way of providing information from the video content of all the cameras in the form of either keyframes or video segments. This paper presents an overview of the existing strategies proposed for MVS, including their advantages and drawbacks. Our survey covers the genericsteps in MVS, such as the pre-processing of video data, feature extraction, and post-processing followed by summary generation. We also describe the datasets that are available for the evaluation of MVS. Finally, we examine the major current issues related to MVS and put forward the recommendations for future research(1). (C) 2020 Elsevier Ltd. All rights reserved.This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2019R1A2B5B01070067)Hussain, T.; Muhammad, K.; Ding, W.; Lloret, J.; Baik, SW.; De Albuquerque, VHC. (2021). A comprehensive survey of multi-view video summarization. Pattern Recognition. 109:1-15. https://doi.org/10.1016/j.patcog.2020.10756711510

Virtual camera synthesis for soccer game replays

International audienceIn this paper, we present a set of tools developed during the creation of a platform that allows the automatic generation of virtual views in a live soccer game production. Observing the scene through a multi-camera system, a 3D approximation of the players is computed and used for the synthesis of virtual views. The system is suitable both for static scenes, to create bullet time effects, and for video applications, where the virtual camera moves as the game plays

Advances in Object and Activity Detection in Remote Sensing Imagery

The recent revolution in deep learning has enabled considerable development in the fields of object and activity detection. Visual object detection tries to find objects of target classes with precise localisation in an image and assign each object instance a corresponding class label. At the same time, activity recognition aims to determine the actions or activities of an agent or group of agents based on sensor or video observation data. It is a very important and challenging problem to detect, identify, track, and understand the behaviour of objects through images and videos taken by various cameras. Together, objects and their activity recognition in imaging data captured by remote sensing platforms is a highly dynamic and challenging research topic. During the last decade, there has been significant growth in the number of publications in the field of object and activity recognition. In particular, many researchers have proposed application domains to identify objects and their specific behaviours from air and spaceborne imagery. This Special Issue includes papers that explore novel and challenging topics for object and activity detection in remote sensing images and videos acquired by diverse platforms

Feature Reduction and Representation Learning for Visual Applications

Computation on large-scale data spaces has been involved in many active problems in computer vision and pattern recognition. However, in realistic applications, most existing algorithms are heavily restricted by the large number of features, and tend to be inefficient and even infeasible. In this thesis, the solution to this problem is addressed in the following ways: (1) projecting features onto a lower-dimensional subspace; (2) embedding features into a Hamming space. Firstly, a novel subspace learning algorithm called Local Feature Discriminant Projection (LFDP) is proposed for discriminant analysis of local features. LFDP is able to efficiently seek a subspace to improve the discriminability of local features for classification. Extensive experimental validation on three benchmark datasets demonstrates that the proposed LFDP outperforms other dimensionality reduction methods and achieves state-of-the-art performance for image classification. Secondly, for action recognition, a novel binary local representation for RGB-D video data fusion is presented. In this approach, a general local descriptor called Local Flux Feature (LFF) is obtained for both RGB and depth data by computing the local fluxes of the gradient fields of video data. Then the LFFs from RGB and depth channels are fused into a Hamming space via the Structure Preserving Projection (SPP), which preserves not only the pairwise feature structure, but also a higher level connection between samples and classes. Comprehensive experimental results show the superiority of both LFF and SPP. Thirdly, in respect of unsupervised learning, SPP is extended to the Binary Set Embedding (BSE) for cross-modal retrieval. BSE outputs meaningful hash codes for local features from the image domain and word vectors from text domain. Extensive evaluation on two widely-used image-text datasets demonstrates the superior performance of BSE compared with state-of-the-art cross-modal hashing methods. Finally, a generalized multiview spectral embedding algorithm called Kernelized Multiview Projection (KMP) is proposed to fuse the multimedia data from multiple sources. Different features/views in the reproducing kernel Hilbert spaces are linearly fused together and then projected onto a low-dimensional subspace by KMP, whose performance is thoroughly evaluated on both image and video datasets compared with other multiview embedding methods

Visual Concept Detection in Images and Videos

The rapidly increasing proliferation of digital images and videos leads to a situation where content-based search in multimedia databases becomes more and more important. A prerequisite for effective image and video search is to analyze and index media content automatically. Current approaches in the field of image and video retrieval focus on semantic concepts serving as an intermediate description to bridge the “semantic gap” between the data representation and the human interpretation. Due to the large complexity and variability in the appearance of visual concepts, the detection of arbitrary concepts represents a very challenging task. In this thesis, the following aspects of visual concept detection systems are addressed: First, enhanced local descriptors for mid-level feature coding are presented. Based on the observation that scale-invariant feature transform (SIFT) descriptors with different spatial extents yield large performance differences, a novel concept detection system is proposed that combines feature representations for different spatial extents using multiple kernel learning (MKL). A multi-modal video concept detection system is presented that relies on Bag-of-Words representations for visual and in particular for audio features. Furthermore, a method for the SIFT-based integration of color information, called color moment SIFT, is introduced. Comparative experimental results demonstrate the superior performance of the proposed systems on the Mediamill and on the VOC Challenge. Second, an approach is presented that systematically utilizes results of object detectors. Novel object-based features are generated based on object detection results using different pooling strategies. For videos, detection results are assembled to object sequences and a shot-based confidence score as well as further features, such as position, frame coverage or movement, are computed for each object class. These features are used as additional input for the support vector machine (SVM)-based concept classifiers. Thus, other related concepts can also profit from object-based features. Extensive experiments on the Mediamill, VOC and TRECVid Challenge show significant improvements in terms of retrieval performance not only for the object classes, but also in particular for a large number of indirectly related concepts. Moreover, it has been demonstrated that a few object-based features are beneficial for a large number of concept classes. On the VOC Challenge, the additional use of object-based features led to a superior performance for the image classification task of 63.8% mean average precision (AP). Furthermore, the generalization capabilities of concept models are investigated. It is shown that different source and target domains lead to a severe loss in concept detection performance. In these cross-domain settings, object-based features achieve a significant performance improvement. Since it is inefficient to run a large number of single-class object detectors, it is additionally demonstrated how a concurrent multi-class object detection system can be constructed to speed up the detection of many object classes in images. Third, a novel, purely web-supervised learning approach for modeling heterogeneous concept classes in images is proposed. Tags and annotations of multimedia data in the WWW are rich sources of information that can be employed for learning visual concepts. The presented approach is aimed at continuous long-term learning of appearance models and improving these models periodically. For this purpose, several components have been developed: a crawling component, a multi-modal clustering component for spam detection and subclass identification, a novel learning component, called “random savanna”, a validation component, an updating component, and a scalability manager. Only a single word describing the visual concept is required to initiate the learning process. Experimental results demonstrate the capabilities of the individual components. Finally, a generic concept detection system is applied to support interdisciplinary research efforts in the field of psychology and media science. The psychological research question addressed in the field of behavioral sciences is, whether and how playing violent content in computer games may induce aggression. Therefore, novel semantic concepts most notably “violence” are detected in computer game videos to gain insights into the interrelationship of violent game events and the brain activity of a player. Experimental results demonstrate the excellent performance of the proposed automatic concept detection approach for such interdisciplinary research