适用于大动态范围场景的自动曝光控制算法

针对数码成像系统在大动态范围场景下存在的对主体曝光不足或曝光过度的问题,本文提出了一种新的自动曝光控制算法.该方法将图像窗口分割成M×N个矩形块,利用大动态范围场景中主体与背景之间所具有的较大的对比度,采用一个主动搜索的过程将主体鉴别出来,随后根据主体与背景之间的亮度关系对光照条件进行判断,并且在计算图像整体亮度水平时赋予主体与背景相应的权重从而更多地反映出主体的信息.实验结果表明,算法对所有的评估场景都做出了正确的光照条件判断,其在大动态范围场景下拍摄的预览帧数和曝光误差分别小于8.8帧和6.56%.     

High-Movement Human Segmentation in Video Using Adaptive N-Frames Ensemble

A wide range of camera apps and online video conferencing services support the feature of changing the background in real-time for aesthetic, privacy, and security reasons. Numerous studies show that the Deep-Learning (DL) is a suitable option for human segmentation, and the ensemble of multiple DL-based segmentation models can improve the segmentation result. However, these approaches are not as effective when directly applied to the image segmentation in a video. This paper proposes an Adaptive N-Frames Ensemble (AFE) approach for high-movement human segmentation in a video using an ensemble of multiple DL models. In contrast to an ensemble, which executes multiple DL models simultaneously for every single video frame, the proposed AFE approach executes only a single DL model upon a current video frame. It combines the segmentation outputs of previous frames for the final segmentation output when the frame difference is less than a particular threshold. Our method employs the idea of the N-Frames Ensemble (NFE) method, which uses the ensemble of the image segmentation of a current video frame and previous video frames. However, NFE is not suitable for the segmentation of fast-moving objects in a video nor a video with low frame rates. The proposed AFE approach addresses the limitations of the NFE method. Our experiment uses three human segmentation models, namely Fully Convolutional Network (FCN), DeepLabv3, and Mediapipe. We evaluated our approach using 1711 videos of the TikTok50f dataset with a single-person view. The TikTok50f dataset is a reconstructed version of the publicly available TikTok dataset by cropping, resizing and dividing it into videos having 50 frames each. This paper compares the proposed AFE with single models and the Two-Models Ensemble, as well as the NFE models. The experiment results show that the proposed AFE is suitable for low-movement as well as high-movement human segmentation in a video.     

A framework for moving object segmentation using dynamic background modelling and shadow suppression in complex wavelet domain

In this paper, a framework for dynamic background modelling and shadow suppression under rapidly changing illumination conditions for moving object segmentation in complex wavelet domain is proposed which deals with the problems of ghosts, object shadows, noise, object distortion in dynamic background changes. The proposed method consists of eight steps applied on given video frames which include: wavelet de-composition of frame using complex wavelet transform; use of change detection on detail coefficients; use of dynamic background modelling on approximate co-efficient; cast shadow suppression; use of soft thresholding for noise removal; strong edge detection; inverse wavelet transformation for reconstruction and finally using closing morphology operator. A comparative analysis of the proposed method is presented both qualitatively and quantitatively with other standard methods available in the literature for four datasets in terms of various performance measures. Experimental results demonstrate the efficacy of the proposed method is better in terms of relative foreground area measure, misclassification penalty, relative position based measure, normalised cross-correlation, normalised absolute error, peak signal-to-noise ratio and pixel classification based measure as compared to other standard methods.     

红外序列图像的支持向量机分割方法

红外序列图像的准确分割是自动目标识别的关键,而当图像背景复杂时,传统的图像分割技术往往难以满足要求,为此,提出了基于支持向量机的红外序列图像分割方法。序列图像中的部分帧被作为训练样本,通过选择适合的模型参数,运用支持向量机方法建立学习机器,将后续图像帧中的目标从复杂的背景中识别出来,从而实现红外图像分割。实际红外序列图像分割表明,基于支持向量机的图像分割方法不需要复杂的预处理和后处理工作,分割效果理想,对于小目标的图像,识别正确率可达 99%。     

基于标记的多尺度分水岭视频目标分割算法

针对视频目标提取的问题,提出了基于标记的多尺度分水岭视频目标分割算法.该算法以帧间变化检测为基础,通过改进的最小Tsallis交叉熵进行去噪、滤波,经形态学处理后得到运动目标初始二值掩模,并利用初始二值掩模得到用于分水岭算法的前景与背景标记,用该标记修正当前帧的多尺度形态学梯度图像,最后进行分水岭分割,得到具有精确边界的视频对象.实验结果表明,该算法能有效地分割和提取视频序列中的单个、多个以及快速运动的目标,继承了变化检测和分水岭算法速度快的优点,克服了分水岭容易产生过分割的缺点,具有较强的适用性.     

一种亮度融合的视频增强方法

为了提高低照度视频的视觉效果,常常利用高质量白天亮度来增强夜间(低照度)视频.本文提出了一种亮度融合的视频增强方法.首先为了获取白天背景,采用了平均K帧的方法,然后使用Retinex理论,提取了白天背景和夜间视频帧的亮度,同时为了增强夜间移动物,采用帧差法提取了夜间视频帧的移动物,最后利用相同场景的白天背景亮度融合夜间...     

快速背景重建的在线运动目标检测

为了能快速地从视频图像序列中创建可靠的背景图像,进而提取运动目标,文中提出了一种基于反馈信息的运动目标检测算法.首先提出了基于相邻帧信息和背景估计信息相融合的背景重建算法,保证了在视频场景改变时仍能迅速捕捉背景;还提出了基于一种在线Otsu法的运动目标检测,将相邻帧运动目标信息反馈到目标提取算法中,弥补传统Otsu法的不足;最后提出了对光线变化具有一定鲁棒性的背景估计算法.实验表明,该方法的重建速度快,准确率高,能满足实时检测的需要.     

Statistically tuned Gaussian background subtraction technique for UAV videos

Background subtraction is one of the efficient techniques to segment the targets from non-informative background of a video. The traditional background subtraction technique suits for videos with static background whereas the video obtained from unmanned aerial vehicle has dynamic background. Here, we propose an algorithm with tuning factor and Gaussian update for surveillance videos that suits effectively for aerial videos. The tuning factor is optimized by extracting the statistical features of the input frames. With the optimized tuning factor and Gaussian update an adaptive Gaussian-based background subtraction technique is proposed. The algorithm involves modelling, update and subtraction phases. This running Gaussian average based background subtraction technique uses updation at both model generation phase and subtraction phase. The resultant video extracts the moving objects from the dynamic background. Sample videos of various properties such as cluttered background, small objects, moving background and multiple objects are considered for evaluation. The technique is statistically compared with frame differencing technique, temporal median method and mixture of Gaussian model and performance evaluation is done to check the effectiveness of the proposed technique after optimization for both static and dynamic videos.     

Moving/motionless foreground object detection using fast statistical background updating

Abstract

In video surveillance, the detection of foreground objects in an image sequence from a still camera is very important for object tracking, activity recognition and behaviour understanding. The conventional background subtraction cannot respond promptly to dynamic changes in the background, and temporal difference cannot accurately extract the object shapes and detect motionless objects. In this paper, we propose a fast statistical process control scheme for foreground segmentation. The proposed method can promptly calculate the exact grey-level mean and standard deviation of individual pixels in both short- and long-term image sequences by simply deleting the earliest one among the set of images and adding the current image scene in the image sequence. A short-term updating process can be highly responsive to dynamic changes of the environment, and a long-term updating process can well extract the shape of a moving object. The detection results from both the short- and long-term processes are incorporated to detect motionless objects and eliminate non-stationary background objects. Experimental results have shown that the proposed scheme can be well applied to both indoor and outdoor environments. It can effectively extract foreground objects with various moving speeds or without motion at a high process frame rate.     

基于差异积累的视频运动对象自动分割

针对视频运动对象的自动分割,本文给出了一种基于差异积累的自动分割算法。与传统的基于运动信息变化检测方法不同,该算法通过累积的帧差信息构建出可靠的背景,与当前帧比较进而提取出视频运动对象。本文提出了一种增强的基于Otsu法的自适应阈值化方法,能更准确地对背景差图像进行阈值化分割,克服了传统Otsu法阈值化容易失效的问题。改进的基于区域生长的定位方法更能避免传统方法的误定位及重定位的问题。实验结果表明,本文算法具有较好的实时性、自适应性以及鲁棒性,可以较为可靠地建立背景模型并进行实时更新,适用于刚体或非刚体存在平缓的光照变化以及摄像头微抖动的视频运动对象的自动分割。     

Tucker visual search-based hybrid tracking model and Fractional Kohonen Self-Organizing Map for anomaly localization and detection in surveillance videos

Anomaly detection (AD) in video is a challenging task employed in the intelligent video surveillance applications. This paper presents a technique for localizing and detecting anomalies in surveillance videos by proposing hybrid tracking model and Fractional Kohonen Self-Organizing Map (FKSOM). At first, the objects in the initial frames are detected by extracting the background and comparing with the succeeding frames. Then, a tracking model is developed to track the objects in the frame. Further, the features, such as object shape, speed, energy, correlation, and homogeneity, are extracted in the feature extraction process. Finally, the proposed FKSOM algorithm performs AD by identifying anomalous and normal events in the frame. The performance of the proposed technique is evaluated using the metrics, such as Multiple Object Tracking Precision (MOTP), accuracy, sensitivity, and specificity, where it obtains MOTP of 0.9895 with an average accuracy of 0.9339, the sensitivity of 0.9288 and specificity of 1.     

灵活可变的运动图像的分割算法

运动图像的分割在基于内容的视频编码及视频检索中占有重要的地位。利用运动图像帧中的时空域信息，提出了一种分割运动对象的方法。其最大的特点是灵活性，可以根据序列的实际情况来选择算法。实验证明分割效果较好并节省了处理时间。     

基于改进BiSeNet的室内场景语义分割方法

室内场景的语义分割一直是深度学习语义分割领域的一个重要方向.室内语义分割主要存在的问题有语义类别多、很多物体类会有相互遮挡、某些类之间相似性较高等.针对这些问题,提出了一种用于室内场景语义分割的方法.该方法在BiSeNet(bilateral segmentation network)的网络结构基础上,引入了一个空洞金...     

Bit Rate Reduction in Cloud Gaming Using Object Detection Technique

The past two decades witnessed a broad-increase in web technology and on-line gaming. Enhancing the broadband confinements is viewed as one of the most significant variables that prompted new gaming technology. The immense utilization of web applications and games additionally prompted growth in the handled devices and moving the limited gaming experience from user devices to online cloud servers. As internet capabilities are enhanced new ways of gaming are being used to improve the gaming experience. In cloud-based video gaming, game engines are hosted in cloud gaming data centers, and compressed gaming scenes are rendered to the players over the internet with updated controls. In such systems, the task of transferring games and video compression imposes huge computational complexity is required on cloud servers. The basic problems in cloud gaming in particular are high encoding time, latency, and low frame rates which require a new methodology for a better solution. To improve the bandwidth issue in cloud games, the compression of video sequences requires an alternative mechanism to improve gaming adaption without input delay. In this paper, the proposed improved methodology is used for automatic unnecessary scene detection, scene removing and bit rate reduction using an adaptive algorithm for object detection in a game scene. As a result, simulations showed without much impact on the players’ quality experience, the selective object encoding method and object adaption technique decrease the network latency issue, reduce the game streaming bitrate at a remarkable scale on different games. The proposed algorithm was evaluated for three video game scenes. In this paper, achieved 14.6% decrease in encoding and 45.6% decrease in bit rate for the first video game scene.     

Moving object detection and tracking in videos through turbulent medium

This paper addresses the problem of identifying and tracking moving objects in a video sequence having a time-varying background. This is a fundamental task in many computer vision applications, though a very challenging one because of turbulence that causes blurring and spatiotemporal movements of the background images. Our proposed approach involves two major steps. First, a moving object detection algorithm that deals with the detection of real motions by separating the turbulence-induced motions using a two-level thresholding technique is used. In the second step, a feature-based generalized regression neural network is applied to track the detected objects throughout the frames in the video sequence. The proposed approach uses the centroid and area features of the moving objects and creates the reference regions instantly by selecting the objects within a circle. Simulation experiments are carried out on several turbulence-degraded video sequences and comparisons with an earlier method confirms that the proposed approach provides a more effective tracking of the targets.     

Spatio-temporal motion-based foreground segmentation and shadow suppression

A relevant problem in computer vision is how to detect and track moving objects from video sequences efficiently. Some algorithms require manual calibration in terms of specification of parameters or some hypotheses. A novel method is developed to extract moving objects through multi-scale wavelet transform across background subtraction. The optimal selection of threshold is automatically determined which does not require any complex supervised training or manual calibration. The proposed approach is efficient in detecting moving objects with low contrast against the background and the detection is less affected by the presence of moving objects in the scene. The developed method combines region connectivity with chromatic consistency to overcome the aperture problem. Ghosts are removed by the proposed background update function, which efficiently prevents undesired corruption of background model and does not consider adaptation coefficient. The mentioned approach is scene-independent and the capacity to extract moving object and suppress cast shadow is high. The developed algorithm is flexible and computationally cost-effective. Experiments show that the proposed approach is robust and efficient in segmenting foreground and suppressing shadow by comparison.     

采用递归门限分析的红外目标分割

提出了一种有效的基于递归门限分析的红外目标分割方法。针对传统方法在目标的相对面积较小时背景信息容易误分的问题,将传统分割方法和递归处理结合起来,用于分割红外目标。在分割时,将每次分割得到的背景部分(即暗部分)淘汰掉,而保留分割得到的目标部分(即亮部分)。对得到的目标部分进行再分割,又得到新的目标和背景部分,如此重复下去,直至得到目标为止。对传统的Otsu方法、一维熵方法、二维熵方法的递归分割特性进行了分析比较,并根据目标的先验知识提出一种合理的递归终止准则。试验结果证明,基于递归门限分析的方法是一种行之有效的目标分割方法,分割性能优于传统方法。     

Real-time action detection and temporal segmentation in continuous video

Temporal segmentation of actions has been under intensive focus in the field of computer vision for a prolonged period. The present study proposed a template-based framework to resolve the issues concerning timeliness and real-time performance in the temporal segmentation in a continuous video. A complete action can be detected, based on the previous frames, and the action can be segmented immediately without waiting for the follow-up frames. Herein, characteristic frames are selected by a martingale-based method, followed by the formation of the corresponding motion history through backtracking along the characteristic frames, and the final segmentation is determined according to the recognition model trained by the extreme learning machine. In the experiment on the IXMAS database, the average rate of the detection of action reached 91%, and the accuracy in the frame level reached 83.5%. In the experiment on the 3D skeleton data based on Kinect, the detection rate reached 94%.     

一种采用背景统计技术的视频对象分割算法

利用背景统计技术从累积的帧差信息中构建出完整、可靠的背景区域,并将其与当前帧相比较,得到初始对象分割掩膜;再对之进行后处理,以消除噪声影响和平滑对象边界,从而获得较好的对象分割掩膜,并提取出视频对象。该算法不需要预知运动对象的形状、数目等,就能较好地从静止背景中分离出目标,实验证明,它具有一定的实用性和鲁棒性。     

一种累积帧差视频对象分割算法

经过对视频序列的多帧累积,利用帧间变化信息可求得帧差之和,并用数学形态学方法处理之后,即可提取出对象边界。由此,提出了一种累积帧差的视频对象分割算法。实验结果表明,该算法能对低比特率视频序列中运动较为缓慢、背景相对简单的对象进行有效的分割,而且所用时间较短,算法简单、高效。