基于宏块类型信息的自适应场景变换检测算法

提出了一种压缩域中利用B帧的宏块类型信息自适应场景变换检测算法,采取滑窗方式自适应地检测场景突变和场景渐变的准确位置。利用电影、动画、新闻等素材作为实验视频库,并利用MPEG 2作为实验平台,取得了较高的查全率和查准率。     

一种快速新闻视频标题字幕探测与定位方法*

新闻视频字幕包含有丰富的语义信息,尤其是标题字幕,对新闻视频高层语义内容的分析和理解具有 重要作用。利用标题字幕的时空分布特征,提出了一个新闻视频标题字幕的快速探测与定位方法。首先利用标 题字幕持续多帧出现的特点降低所需处理的帧数,然后基于标题字幕的边缘特征和位置特征,标记帧图像的候 选字幕块,对帧序列中的图像进行统计分析,探测出视频中标题字幕的位置及出现消失时间。实验结果表明所 提方法简单有效,能够快速、鲁棒地探测并定位新闻视频中的标题字幕。     

Fast scene change detection using direct feature extraction fromMPEG compressed videos

In order to process video data efficiently, a video segmentation technique through scene change detection must be required. This is a fundamental operation used in many digital video applications such as digital libraries, video on demand (VOD), etc. Many of these advanced video applications require manipulations of compressed video signals. So, the scene change detection process is achieved by analyzing the video directly in the compressed domain, thereby avoiding the overhead of decompressing video into individual frames in the pixel domain. In this paper, we propose a fast scene change detection algorithm using direct feature extraction from MPEG compressed videos, and evaluate this technique using sample video data, First, we derive binary edge maps from the AC coefficients in blocks which were discrete cosine transformed. Second, we measure edge orientation, strength and offset using correlation between the AC coefficients in the derived binary edge maps. Finally, we match two consecutive frames using these two features (edge orientation and strength). This process was made possible by a new mathematical formulation for deriving the edge information directly from the discrete cosine transform (DCT) coefficients. We have shown that the proposed algorithm is faster or more accurate than the previously known scene change detection algorithms     

Spatio-temporal analysis for obstacle detection in agricultural videos

Autonomous mobile vehicles are becoming commoner in outdoor scenarios for agricultural applications. They must be equipped with a robot navigation system for sensing, mapping, localization, path planning, and obstacle avoidance. In autonomous vehicles, safety becomes a major challenge where unexpected obstacles in the working area must be conveniently addressed. Of particular interest are, people or animals crossing in front of the vehicle or fixed/moving uncatalogued elements in specific positions. Detection of unexpected obstacles or elements on video sequences acquired with a machine vision system on-board a tractor moving in cornfields makes the main contribution to this research. We propose a new strategy for automatic video analysis to detect static/dynamic obstacles in agricultural environments via spatial-temporal analysis. At a first stage obstacles are detected by using spatial information based on spectral colour analysis and texture data. At a second stage temporal information is used to detect moving objects/obstacles at the scene, which is of particular interest in camouflaged elements within the environment. A main feature of our method is that it does not require any training process. Another feature of our approach consists in the spatial analysis to obtain an initial segmentation of interesting objects; afterwards, temporal information is used for discriminating between moving and static objects. To the best of our knowledge in the field of agricultural image analysis, classical approaches make use of either spatial or temporal information, but not both at the same time, making an important contribution. Our method shows favourable results when tested in different outdoor scenarios in agricultural environments, which are really complex, mainly due to the high variability in the illumination conditions, causing undesired effects such as shadows and alternating lighted and dark areas. Dynamic background, camera vibrations and static and dynamic objects are also factors complicating the situation. The results are comparable to those obtained with other state-of-art techniques reported in literature.     

一种新的近重复监控视频检测算法

设计了一种使用视频镜头时序特征来实现级联式检测近重复视频的算法.首先在进行关键帧特征提取之前,直接在镜头层次上提取时序特征,初步滤除完全不相同的视频,然后对剩下的视频帧提取全局颜色特征和SURF特征进行逐步检测,最终获得与查询视频近重复的视频.对实验室的监控视频进行小范围的验证实验,实验结果表明,该算法与不用时序特征的方法相比有一定的有效性和准确性.     

Combining experts for anchorperson shot detection in news videos

Automatic classification of shots extracted by news videos plays an important role in the context of news video segmentation, which is an essential step towards effective indexing of broadcasters digital databases. In spite of the efforts reported by the researchers involved in this field, no techniques providing fully satisfactory performance have been presented until now. In this paper, we propose a multi-expert approach for unsupervised shot classification. The proposed multi-expert system (MES) combines three algorithms that are model-free and do not require a specific training phase. In order to assess the performance of the MES, we built up a database significantly wider than those typically used in the field. Experimental results demonstrate the effectiveness of the proposed approach both in terms of shot classification and of news story detection capability.     

面向人脸修复篡改检测的大规模数据集

目的 图像合成方法随着计算机视觉的不断发展和深度学习技术的逐渐成熟为人们的生活带来了丰富的体验。然而,用于传播虚假信息的恶意篡改图像可能对社会造成极大危害,使人们对数字内容在图像媒体中的真实性产生怀疑。面部编辑作为一种常用的图像篡改手段,通过修改面部的五官信息来伪造人脸。图像修复技术是面部编辑常用的手段之一,使用其进行面部伪造篡改同样为人们的生活带来了很大干扰。为了对此类篡改检测方法的相关研究提供数据支持,本文制作了面向人脸修复篡改检测的大规模数据集。方法 具体来说,本文选用了不同质量的源数据集（高质量的人脸图像数据集CelebA-HQ及低质量的人脸视频数据集FF++）,通过图像分割方法将面部五官区域分割,最后使用两种基于深度网络的修复方法CTSDG（image inpainting via conditional texture and structure dual generation）和RFR（recurrent feature reasoning for image inpainting）以及一种传统修复方法SC（struct completion）,生成总数量达到60万幅的大规模修复图像数据集。结果 实验结果表明,由FF++数据集生成的图像在基准检测网络ResNet-50下的检测精度下降了15%,在Xception-Net网络下检测精度下降了5%。且不同面部部位的检测精度相差较大,其中眼睛部位的检测精度最低,检测精度为0.91。通过泛化性实验表明,同一源数据集生成的数据在不同部位的修复图像间存在一定的泛化性,而不同的源数据制作的数据集间几乎没有泛化性。因此,该数据集也可为修复图像之间的泛化性研究提供研究数据,可以在不同数据集、不同修复方式和不同面部部位生成的图像间进行修复图像的泛化性研究。结论 基于图像修复技术的篡改方式在一定程度上可以骗过篡改检测器,对于此类篡改方式的检测方法研究具有现实意义。提供的大型基于修复技术的人脸篡改数据集为该领域的研究提供了新的数据来源,丰富了数据多样性,为深入研究该类型的人脸篡改和检测方法提供了有力的基准。数据集开源地址https://pan.baidu.com/s/1-9HIBya9X-geNDe5zcJldw?pwd=thli。     

不平衡入侵检测数据的代价敏感分类策略*

提出一种新的预处理算法AdaP,不仅有效避免了数据过度拟合,且可独立使用。针对不平衡的入侵检测数据集,引入代价敏感机制,基于权值矩阵最小化误分类代价的思想,去除部分训练密集区域、拓展稀疏区域的同时再过滤噪声,最终实现了AdaP算法与AdaCost算法相结合的策略。实验证明此策略充分体现了提升算法有效提升前端弱分类算法分类精度和预处理算法平衡稀有类数据的优势,且可有效提高不平衡入侵检测数据的分类性能。     

Multi-domain anomaly detection in spatial datasets

A spatial anomaly captures a phenomenon occurring in a region which is vastly deviant in behavior with respect to the other normal observations. However, in reality this anomaly may impact other phenomena in the region across multiple domains, for example, crime is often linked to other sociopolitical factors or phenomenon such as poverty and education. Similarly, accidents in the region may be linked to other environmental factors such as weather and surface condition. So, finding anomalies across multiple domains is important in various applications. In this paper, we propose an approach for finding such a tangible anomalous window across multiple domains where window refers to the set of contiguous points in space, and since the window is multi-domain, there are several overlapping windows in the same space across domains. Our approach for finding anomalous window across the domains comprises the following steps: (1) single-domain anomaly detection: discovering anomalous window in each domain; (2) association rule mining: discovering relationship between the anomalous windows across domains using association rule mining; and (3) validation: validating the result using (a) Monte Carlo simulation, (b) correlation using lift and (c) ground truth evaluation. In addition, we also provide a probabilistic framework to evaluate the relationships between the spatial nodes as a postprocessing step. Finally, we provide a visualization technique for viewing the multi-domain anomalous window and the probabilistic relationships between the nodes. We provide detailed experimental results and comparisons with other approaches using real-world health ranking [51] and transportation datasets [50] with known ground truth windows. The results show that our approach is effective in finding the anomalies in multiple domains as compared to other approaches.     

Spatiotemporal optical blob reconstruction for object detection in grayscale videos

Multimedia Tools and Applications - There has been a significant research devoted towards detection of a moving object in an image sequence. Detected moving objects usually contain some errors...     

Real-time no-reference histogram-based freezing artifact detection algorithm for UHD videos

Multimedia Tools and Applications - During video transmission different errors can occur, which can introduce distinct artifacts in video received at the end user side. One of the most common...     

Highlights modeling and detection in sports videos

Automatic annotation of semantic events allows effective retrieval of video content. In this work, we present solutions for highlights detection in sports videos. This application is particularly interesting for broadcasters, since they extensively use manual annotation to select interesting highlights that are edited to create new programmes. The proposed approach exploits the typical structure of a wide class of sports videos, namely, those related to sports which are played in delimited venues with playfields of well known geometry, like soccer, basketball, swimming, track and field disciplines, and so on. For this class of sports, a modeling scheme based on a limited set of visual cues and on finite state machines (FSM) that encode the temporal evolution of highlights is presented. Algorithms for model checking and for visual cues estimation are discussed, as well as applications of the representation to different sport domains.     

视频中人脸位置的定量检测

现有的人脸检测评判标准通常情况下仅仅只是对人脸有无的定性检测,对于视频中人脸位置的定量描述并没有严格的规范;另外,现在的一些研究如视频人脸替换等对视频流中人脸位置的连续性有较高的要求。为了解决上述两个问题,相比之前的人脸检测以及人脸跟踪评估标准,提出了一种视频中人脸位置的定量检测评估标准,并且提出了一种视频中人脸位置的检测方法。该方法首先通过改进的Haar-Like级联分类器在目标区域中检测到人脸初始位置;然后采用金字塔光流法对人脸位置进行预测,同时引入正反向误差检测机制实现对结果的自检测,最终确定人脸位置。实验结果表明,检测标准能够对测试算法在视频人脸检测的定量描述结果给出评判,提出的检测算法在人脸位置的时间一致性上有所提升。     

加速大规模数据集的离群点检测

针对现有基于距离的离群点检测算法在处理大规模数据时效率低的问题,提出一种基于聚类和索引的分布式离群点检测(DODCI) 算法。首先利用聚类方法将大数据集划分成簇;然后在分布式环境中的各节点处并行创建各个簇的索引;最后使用两个优化策略和两条剪枝规则以循环的方式在各节点处进行离群点检测。在合成数据集和整理后的KDD CUP数据集上的实验结果显示,在数据量较大时该算法比Orca和iDOoR算法快近一个数量级。理论和实验分析表明,该算法可以有效提高大规模数据中离群点的检测效率。     

iSurveillance: Intelligent framework for multiple events detection in surveillance videos

Research in the video surveillance is gaining more popularity due to its widespread applications as well as social impact. In this paper, we present an intelligent framework for detection of multiple events in surveillance videos. Based on the principle of compositionality, we modularize the surveillance problems into a set of variables comprising regions-of-interest, classes (i.e. human, vehicle), attributes (i.e. speed, locality) and a set of notions (i.e. rules) associated to each of the attributes to construct a knowledge-based understanding of the environment. The final output from the reasoning process, which combines the definition domains of the various variables, allows a broader and integrated understanding of complex pattern of activities in the scene. This is in contrast to the state-of-the-art solutions that are only able to perform only a singular task, at a time. Experimental results on both the public and real-time datasets have demonstrated the effectiveness and robustness of the proposed framework in detecting multiple events in surveillance videos.     

基于Adaboost的选择性样本权重更新算法

提出一种选择性样本权重更新算法 ,把 FNR和 FPR引入样本权重更新过程 ,将分类效果反馈给分类器,实现对分类器结构的有效控制 ,在样本权重更新时合理选择更新方法 ,使得分类器的 FNR或 FPR达到理想状态。实验表明 ,该算法能有效改善整个分类器的 FNR和 FPR。     

近岸海浪视频浪高自动检测

目的 目前基于视觉信息的海浪要素检测方法分为基于立体视觉和基于视频/图像特征的检测方法,前者对浪高的解析不稳定、模型复杂、鲁棒性较差、不能很好地满足实际应用的需求,后者主要检测海浪的运动方向和浪高等级,无法获取精确的浪高值,其中基于图像特征的检测受限于先验知识,检测稳定性较差。为此,本文结合深度学习的特征学习机制,提出了一种面向近岸海浪视频的浪高自动检测方法。方法 从近岸海浪监控视频中提取视频帧图像,计算相邻两帧差分获取差分图像,通过数据预处理对静态图像集和差分图像集进行数据扩充;针对两类图像集分别设计多层局部感知卷积神经网络NIN（network in network）结构并预训练网络模型;分别用预训练的网络模型提取静态图像和差分图像的高层特征来表达空间和时间维度的信息,并融合两类特征;通过预训练支持向量回归SVR（support vactor regerssion）模型完成浪高的自动检测。结果 实验结果表明,本文近岸海浪视频浪高检测方法在浪高检测上的平均绝对误差为0.109 5 m,平均相对误差为7.39%;从不同绝对误差范围内的测试集精度上可以看出,基于时间和空间信息融合的回归模型精度变化更加平稳,基于空间信息的NIN模型的精度变化幅度较大,因此本文方法有较好的检测稳定性。结论 通过预训练卷积神经网络提取近岸视频图像时间和空间信息融合的方式,有效弥补了人工设计特征的不完备性,对近岸视频的浪高检测具有较强的鲁棒性,在业务化检测需求范围内（浪高平均相对误差≤ 20%）有着较好的实用性。     

Permutation-test-based clustering method for detection of dynamic patterns in Spatio-temporal datasets

Massive spatio-temporal data have been collected from the earth observation systems for monitoring the changes of natural resources and environment. To find the interesting dynamic patterns embedded in spatio-temporal data, there is an urgent need for detecting spatio-temporal clusters formed by objects with similar attribute values occurring together across space and time. Among different clustering methods, the density-based methods are widely used to detect such spatio-temporal clusters because they are effective for finding arbitrarily shaped clusters and rely on less priori knowledge (e.g. the cluster number). However, a series of user-specified parameters is required to identify high-density objects and to determine cluster significance. In practice, it is difficult for users to determine the optimal clustering parameters; therefore, existing density-based clustering methods typically exhibit unstable performance. To overcome these limitations, a novel density-based spatio-temporal clustering method based on permutation tests is developed in this paper. High-density objects and cluster significance are determined based on statistical information on the dataset. First, the density of each object is defined based on the local variance and a fast permutation test is conducted to identify high-density objects. Then, a proposed two-stage grouping strategy is implemented to group high-density objects and their neighbors; hence, spatio-temporal clusters are formed by minimizing the inhomogeneity increase. Finally, another newly developed permutation test is conducted to evaluate the cluster significance based on the cluster member permutation. Experiments on both simulated and meteorological datasets show that the proposed method exhibits superior performance to two state-of-the-art clustering methods, i.e., ST-DBSCAN and ST-OPTICS. The proposed method can not only identify inherent cluster patterns in spatio-temporal datasets, but also greatly alleviates the difficulty in selecting appropriate clustering parameters.     

电影视频景别音阶的识别与检测

通过对电影景别音阶的识别检测,可以有效地分析和检索电影视频情感变化的片段.在系统分析以往研究成果的基础上,利用电影领域知识构建了局部运动占有率、摄像机运动和镜头间相似度等新的特征, 结合常用的视频特征, 采用贝叶斯分类器来识别电影视频的镜头景别, 并根据景别变化同观众情感之间的关系, 设计了5种能够激发观众情感的景别音阶, 在景别识别的基础上实现了对景别音阶的检测.实验结果表明, 选取的特征能够得到较好的检测结果, 与其他方法相比, 远景与近景的识别在准确率和查全率上均有不同程度的提高.