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.     

An Adaptive Classifier Based Approach for Crowd Anomaly Detection

Crowd Anomaly Detection has become a challenge in intelligent video surveillance system and security. Intelligent video surveillance systems make extensive use of data mining, machine learning and deep learning methods. In this paper a novel approach is proposed to identify abnormal occurrences in crowded situations using deep learning. In this approach, Adaptive GoogleNet Neural Network Classifier with Multi-Objective Whale Optimization Algorithm are applied to predict the abnormal video frames in the crowded scenes. We use multiple instance learning (MIL) to dynamically develop a deep anomalous ranking framework. This technique predicts higher anomalous values for abnormal video frames by treating regular and irregular video bags and video sections. We use the multi-objective whale optimization algorithm to optimize the entire process and get the best results. The performance parameters such as accuracy, precision, recall, and F-score are considered to evaluate the proposed technique using the Python simulation tool. Our simulation results show that the proposed method performs better than the conventional methods on the public live video dataset.     

Spatial-Resolution Independent Object Detection Framework for Aerial Imagery

Earth surveillance through aerial images allows more accurate identification and characterization of objects present on the surface from space and airborne platforms. The progression of deep learning and computer vision methods and the availability of heterogeneous multispectral remote sensing data make the field more fertile for research. With the evolution of optical sensors, aerial images are becoming more precise and larger, which leads to a new kind of problem for object detection algorithms. This paper proposes the “Sliding Region-based Convolutional Neural Network (SRCNN),” which is an extension of the Faster Region-based Convolutional Neural Network (RCNN) object detection framework to make it independent of the image’s spatial resolution and size. The sliding box strategy is used in the proposed model to segment the image while detecting. The proposed framework outperforms the state-of-the-art Faster RCNN model while processing images with significantly different spatial resolution values. The SRCNN is also capable of detecting objects in images of any size.     

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.     

一种室内视频监控图像序列编码方法

在实际高分辨率室内视频监控中,运动物体构成了视频监控的主要内容.本文基于感兴趣区域的检测与恢复,提出了一种高分辨率室内视频监控图像序列的编码新方法,通过对象提取实现基本层和ROI层双层码流传输,其中基本层实现低分辨率图像内容传输,而ROI层用以进行高分辨率图像恢复.新方法有效地降低了视频编码计算复杂度、提高了编码效率.     

Selection of keyframes for video colourization using steerable filtering

The appropriate selection of distinctive keyframes to represent the salient contents of a video is a critical task in video processing applications that rely on content analysis or information retrieval. Although many of the existing keyframe selection techniques perform satisfactorily in capturing salient visual contents, they often fail to adequately highlight the changes in visual information brought about by motion of objects between frames. In this paper, we propose a technique for keyframe selection by formulating the dissimilarity between the frames of a video shot in terms of the change in orientations that the corresponding objects of the two frames have undergone due to motion. This is accomplished by steerable filtering of the frames in order to extract the information about the local orientation of pixels within each frame. The frame to frame dissimilarity is adaptively thresholded over a group of frames in order to select the keyframes. In essence, keyframes are selected at the temporal instances where the change in orientation attains local maxima. Our keyframe selection methodology is specifically relevant to video colourization due to the fact that the keyframes that are to be employed for colourization must be chosen such that they capture all orientational changes effectively, while ensuring adequate content coverage.     

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.     

Adaptive real-time motion segmentation technique based on statistical background model

Motion segmentation is a crucial step for video analysis and has many applications. This paper proposes a method for motion segmentation, which is based on construction of statistical background model. Variance and Covariance of pixels are computed to construct the model for scene background. We perform average frame differencing with this model to extract the objects of interest from the video frames. Morphological operations are used to smooth the object segmentation results. The proposed technique is adaptive to the dynamically changing background because of change in the lighting conditions and in scene background. The method has the capability to relearn the background to adapt these variations. The immediate advantage of the proposed method is its high processing speed of 30 frames per second on large sized (high resolution) videos. We compared the proposed method with other five popular methods of object segmentation in order to prove the effectiveness of the proposed technique. Experimental results demonstrate the novelty of the proposed method in terms of various performance parameters. The method can segment the video stream in real-time, when background changes, lighting conditions vary, and even in the presence of clutter and occlusion     

非参数核密度估计视频目标空域定位技术研究

针对智能视频监控场合对视频运动目标定位的需求,本文提出了一种基于非参数核密度估计的视频运动目标空域定位技术.该技术先对代表视频运动目标的前景样本点进行非参数核密度估计,选择具有最高密度指标的样本点为第一个目标中心,然后通过修正样本点的密度估计值,逐步实现对视频运动目标的空域定位.本文的方法是减法聚类视频运动目标定位技术的进一步推广.推广后的定位方法,可根据具体的目标定位场合,灵活选择核函数对样本点进行核密度估计.实验表明,本文方法具有良好定位效果,同时,在样本点的密度估计上更具灵活性.     

一种视频图像序列中运动对象的分割与跟踪算法

本文提出了一种视频图像序列中运动对象的分割与跟踪算法。该算法通过Canny算子检测出差帧图像的边缘信息,并结合当前帧与背景帧的边缘图像,提取出运动对象。在后续帧中通过建立前帧感兴趣运动对象与当前帧中各运动对象的帧间向量来跟踪当前帧中感兴趣的视频对象。实验结果表明,该算法可行,而且由于该算法简单、计算复杂度小,能很好地满足实时监控系统中对感兴趣运动对象的提取与跟踪。     

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.     

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...     

Signal Processing in Noise Radar Technology [Editorial]

Noise Radar Technology (NRT) addresses the increasing performance demands placed on both military and civilian radar systems by enhancing their reliable operation in congested, unfriendly or hostile environments. For instance, many surface and maritime radar systems used for national border surveillance, traffic control, indoor surveillance, etc. must be able to operate in a congested electromagnetic environment without performance degradations caused by intentional external electromagnetic interference (EMI). Even greater demands are placed on military air, space, maritime and battlefield radar systems that must reliably operate in congested and hostile environments characterized by deliberate and adaptive jamming. In addition, many military missions require covert radar operation, which increases the complexity of radar signal processing.     

Super-resolution video generation algorithm for surveillance applications

Video surveillance is one of the major applications where high-resolution (HR) images are crucial. Since the video camera has limited spatial and temporal resolution, there is a need for super resolution video generation algorithms. In this paper, we have presented a novel technique for activity detection in the surveillance video. To achieve this goal, we have proposed and investigated efficient algorithms for Video Object Plane (VOP) generation, shadow removal from VOP and super-resolved VOP generation, for activity detection from surveillance video. The proposed VOP generation algorithm is computationally efficient and works for both dynamic and static backgrounds. The novel shadow removal algorithm for the VOP is based on texture and its performance has been studied based on average shadow detection and discrimination rates. The proposed super-resolution video generation algorithm has been designed using edge models. The performance of this algorithm has been evaluated using a numerical analysis technique and is found to be better than bi-cubic and bi-linear interpolation techniques.     

对光照变化鲁棒的一种运动目标检测方法

鉴于现有的运动目标检测方法对光照变化的敏感性,本文提出了一种在静态场景下对光照变化鲁棒的运动目标检测方法。该方法利用视频当前图像帧的边缘与背景图像的边缘做差分运算,得出运动目标的轮廓,进而对运动目标进行定位与检测。实验证明,本方法在一定程度上能够消除由于环境光照变化引起的"曝光"现象,实时准确地检测出运动目标及其位置,运算速度快,满足实时性的需求。     

The societal impact of commercial drones

The use of drones or Unmanned Aerial Vehicles (UAVs) in commercial applications has the potential to dramatically alter several industries, and, in the process, change our attitudes and behaviors regarding their impact on our daily lives. The emergence of drones challenges traditional notions of safety, security, privacy, ownership, liability, and regulation. With their ability to collect data and transport loads, drones are re-shaping the way we think and feel about our physical environment. However, they also burdened with the perception as being surveillance equipment, and their commercial use has been criticizied by both individuals and activist organizations. In parallel, drones have been legitimized by regulations and licenses from federal agencies, are used by companies for surveying, inspecting, and imaging, and their technological development are driven by active communities of hobbyists and enthusiasts. This tension presents unique challenges to their integration in the currently existing public, governmental and private infrastructure. In this paper, we will take a look at a few of these issues to understand how drones influence society, and present reccomendations for practitioners, policy makers, and reseachers studying this phenomenon.     

Lifeguards in the sky: Examining the public acceptance of beach-rescue drones

The use of rescue drones is expected to increase in forthcoming years. However, the success of their implementation through different applications will depend on public acceptance. Studies to date have analyzed public support for the use of drones with various applications, although public acceptance of drones in specific contexts remains to be explored. In particular, the use of drones for beach rescues has proven beneficial in reducing response times, thus helping to save lives. In this study, we analyze the public acceptance of lifesaving drones and their associated variables. Data collected from a survey of beach users (N = 3363) for this study are used to measure public acceptance of rescue drones. We found that public acceptance of rescue drones is moderate, with approximately half of all participants accepting their use. In terms of influencing variables, we found that the factors most associated with their use are ‘perceived benefits’ and ‘perceived risks’. We also found that the participants from beaches without lifeguard services were more likely to accept the use of rescue drones. These results initiated a discussion on the variables that are associated with the public acceptance in the specific context of lifesaving. In addition, based on the results of this study, we propose implementation plans for rescue drones that might also include public information campaigns on their benefits for beach users.     

欧拉弹力模型的视频运动目标自适应图切检测

传统的视频运动目标图切检测算法基于低阶马尔科夫随机场,能量函数的低阶近似无法准确描述图像像素的空间相关性,导致图切检测结果过度平滑。本文提出一种基于高阶欧拉弹力模型的图切检测算法,利用欧拉弹性模型优化目标边界曲线和修正能量函数的低阶近似。算法通过利用前一帧图像的检测结果,对当前帧图像运动目标像素点数和前景背景邻接像素对数进行卡尔曼预测,并不断自适应调整当前帧的图像模型参数,实现了视频运动目标的连续全局优化检测。实验结果验证了欧拉弹力模型在视频运动目标检测中的有效性,其检测结果能够更好地满足人的视觉效果。     

网络带宽自适应的远程视频监控系统

当前的远程视频监控系统在一定程度上不能很好地适应网络带宽的变化,无法在多种网络线路上传输视频流。为解决这一问题,本文提出了网络带宽自适应的监控系统结构模型,使用位率控制精度较高的编码器以适应网络带宽的变化,并对网络状况动态进行检测。完成了远程视频监控系统BHJK,该系统可在IP宽带网、电话线及其混和线路上传输视频流,提供基于视频分析的智能运动检测和异常报警。     

A Drones Optimal Path Planning Based on Swarm Intelligence Algorithms

The smart city comprises various interlinked elements which communicate data and offers urban life to citizen. Unmanned Aerial Vehicles (UAV) or drones were commonly employed in different application areas like agriculture, logistics, and surveillance. For improving the drone flying safety and quality of services, a significant solution is for designing the Internet of Drones (IoD) where the drones are utilized to gather data and people communicate to the drones of a specific flying region using the mobile devices is for constructing the Internet-of-Drones, where the drones were utilized for collecting the data, and communicate with others. In addition, the SIRSS-CIoD technique derives a tuna swarm algorithm-based clustering (TSA-C) technique to choose cluster heads (CHs) and organize clusters in IoV networks. Besides, the SIRSS-CIoD technique involves the design of a biogeography-based optimization (BBO) technique to an optimum route selection (RS) process. The design of clustering and routing techniques for IoD networks in smart cities shows the novelty of the study. A wide range of experimental analyses is carried out and the comparative study highlighted the improved performance of the SIRSS-CIoD technique over the other approaches.