Adaptive Human Tracking Across Non-overlapping Cameras in Depression Angles

To track human across non-overlapping cameras in depression angles for applications such as multi-airplane visual human tracking and urban multi-camera surveillance,an adaptive human tracking method is proposed,focusing on both feature representation and human tracking mechanism.Feature representation describes individual by using both improved local appearance descriptors and statistical geometric parameters.The improved feature descriptors can be extracted quickly and make the human feature more discriminative.Adaptive human tracking mechanism is based on feature representation and it arranges the human image blobs in field of view into matrix.Primary appearance models are created to include the maximum inter-camera appearance information captured from different visual angles.The persons appeared in camera are first filtered by statistical geometric parameters.Then the one among the filtered persons who has the maximum matching scale with the primary models is determined to be the target person.Subsequently,the image blobs of the target person are used to update and generate new primary appearance models for the next camera,thus being robust to visual angle changes.Experimental results prove the excellence of the feature representation and show the good generalization capability of tracking mechanism as well as its robustness to condition variables.

Human tracking in camera network with non-overlapping FOVs

An adaptive human tracking method across spatially separated surveillance cameras with non-overlapping fields of views （FOVs） is proposed. The method relies on the two cues of the human appearance model and spatio-temporal information between cameras. For the human appearance model, an HSV color histogram is extracted from different human body parts （head, torso, and legs）, then a weighted algorithm is used to compute the similarity distance of two people. Finally, a similarity sorting algorithm with two thresholds is exploited to find the correspondence. The spatio- temporal information is established in the learning phase and is updated incrementally according to the latest correspondence. The experimental results prove that the proposed human tracking method is effective without requiring camera calibration and it becomes more accurate over time as new observations are accumulated.

Adaptive topology learning of camera network across non-overlapping views

An adaptive topology learning approach is proposed to learn the topology of a practical camera network in an unsupervised way. The nodes are modeled by the Gaussian mixture model. The connectivity between nodes is judged by their cross-correlation function, which is also used to calculate their transition time distribution. The mutual information of the connected node pair is employed for transition probability calculation. A false link eliminating approach is proposed, along with a topology updating strategy to improve the learned topology. A real monitoring system with five disjoint cameras is built for experiments. Comparative results with traditional methods show that the proposed method is more accurate in topology learning and is more robust to environmental changes.

An Improved Hybrid Genetic Algorithm for Chemical Plant Layout Optimization with Novel Non-overlapping and Toxic Gas Dispersion Constraints

New approaches for facility distribution in chemical plants are proposed including an improved non-overlapping constraint based on projection relationships of facilities and a novel toxic gas dispersion constraint. In consideration of the large number of variables in the plant layout model, our new method can significantly reduce the number of variables with their own projection relationships. Also, as toxic gas dispersion is a usual incident in a chemical plant, a simple approach to describe the gas leakage is proposed, which can clearly represent the constraints of potential emission source and sitting facilities. For solving the plant layout model, an improved genetic algorithm (GA) based on infeasible solution fix technique is proposed, which improves the globe search ability of GA. The case study and experiment show that a better layout plan can be obtained with our method, and the safety factors such as gas dispersion and minimum distances can be well handled in the solution.

Object Matching Across Multiple Non-overlapping Fields of View Using Fuzzy Logic

An approach based on fuzzy logic for matching both articulated and non-articulated objects across multiple non-overlapping field of views (FoVs) from multiple cameras is proposed. We call it fuzzy logic matching algorithm (FLMA). The approach uses the information of object motion, shape and camera topology for matching objects across camera views. The motion and shape information of targets are obtained by tracking them using a combination of ConDensation and CAMShift tracking algorithms. The information of camera topology is obtained and used by calculating the projective transformation of each view with the common ground plane. The algorithm is suitable for tracking non-rigid objects with both linear and non-linear motion. We show videos of tracking objects across multiple cameras based on FLMA. From our experiments, the system is able to correctly match the targets across views with a high accuracy.

A novel non-overlapping approach to accurately represent 2D arbitrary particles for DEM modelling

Mechanical behaviors of granular materials are complicated and greatly influenced by the particle shape.Current,some composite approaches have been proposed for realistic particle shape modelling within discrete element method(DEM),while they cannot give a good representation to the shape and mass properties of a real particle.In this work,a novel algorithm is developed to model an arbitrary particle using a cluster of non-overlapping disks.The algorithm mainly consists of two components:boundary filling and domain filling.In the boundary filling,some disks are placed along the boundary for a precise representation of the particle shape,and some more disks are placed in the domain to give an approximation to the mass properties of the particle in the domain filling.Besides,a simple method is proposed to correct the mass properties of a cluster after domain filling and reduce the number of the disks in a cluster for lower computational load.Moreover,it is another great merit of the algorithm that a cluster generated by the algorithm can be used to simulate the particle breakage because of no overlaps between the disks in a cluster.Finally,several examples are used to show the robust performance of the algorithm.A current FORTRAN version of the algorithm is available by contacting the author.

Boundary Element Method with Non-overlapping Domain Decomposition for Diffusion Equation

A boundary element method based on non-overlapping domain decomposition method to solve the time-dependent diffusion equations is presented. The time-dependent fundamental solution is used in the formulation of boundary integrals and the time integration process always restarts from the initial time condition. The process of replacing the interface values, which needs a summation of boundary integrals related to the boundary values at previous time steps can be treated in parallel iterative procedure. Numerical experiments demonstrate that the implementation of the present algorithm is efficient.

Parallel Implementation of the Non-Overlapping Template Matching Test Using CUDA

NIST(National Institute of Standards and Technology) statistical test recognized as the most authoritative is widely used in verifying the randomness of binary sequences. The Non-overlapping Template Matching Test as the 7 th test of the NIST Test Suit is remarkably time consuming and the slow performance is one of the major hurdles in the testing process. In this paper, we present an efficient bit-parallel matching algorithm and segmented scan-based strategy for execution on Graphics Processing Unit(GPU) using NVIDIA Compute Unified Device Architecture(CUDA). Experimental results show the significant performance improvement of the parallelized Non-overlapping Template Matching Test, the running speed is 483 times faster than the original NIST implementation without attenuating the test result accuracy.

Source Camera Identification Algorithm Based on Multi-Scale Feature Fusion

The widespread availability of digital multimedia data has led to a new challenge in digital forensics.Traditional source camera identification algorithms usually rely on various traces in the capturing process.However,these traces have become increasingly difficult to extract due to wide availability of various image processing algorithms.Convolutional Neural Networks(CNN)-based algorithms have demonstrated good discriminative capabilities for different brands and even different models of camera devices.However,their performances is not ideal in case of distinguishing between individual devices of the same model,because cameras of the same model typically use the same optical lens,image sensor,and image processing algorithms,that result in minimal overall differences.In this paper,we propose a camera forensics algorithm based on multi-scale feature fusion to address these issues.The proposed algorithm extracts different local features from feature maps of different scales and then fuses them to obtain a comprehensive feature representation.This representation is then fed into a subsequent camera fingerprint classification network.Building upon the Swin-T network,we utilize Transformer Blocks and Graph Convolutional Network(GCN)modules to fuse multi-scale features from different stages of the backbone network.Furthermore,we conduct experiments on established datasets to demonstrate the feasibility and effectiveness of the proposed approach.

Ultrafast pulse-dilation framing camera and its application for time-resolved X-ray diagnostic

An ultrafast framing camera with a pulse-dilation device,a microchannel plate(MCP)imager,and an electronic imaging system were reported.The camera achieved a temporal resolution of 10 ps by using a pulse-dilation device and gated MCP imager,and a spatial resolution of 100μm by using an electronic imaging system comprising combined magnetic lenses.The spatial resolution characteristics of the camera were studied both theoretically and experimentally.The results showed that the camera with combined magnetic lenses reduced the field curvature and acquired a larger working area.A working area with a diameter of 53 mm was created by applying four magnetic lenses to the camera.Furthermore,the camera was used to detect the X-rays produced by the laser-targeting device.The diagnostic results indicated that the width of the X-ray pulse was approximately 18 ps.

Monocular Distance Estimated Based on PTZ Camera

This paper introduces an intelligent computational approach for extracting salient objects fromimages and estimatingtheir distance information with PTZ (Pan-Tilt-Zoom) cameras. PTZ cameras have found wide applications innumerous public places, serving various purposes such as public securitymanagement, natural disastermonitoring,and crisis alarms, particularly with the rapid development of Artificial Intelligence and global infrastructuralprojects. In this paper, we combine Gauss optical principles with the PTZ camera’s capabilities of horizontal andpitch rotation, as well as optical zoom, to estimate the distance of the object.We present a novel monocular objectdistance estimation model based on the Focal Length-Target Pixel Size (FLTPS) relationship, achieving an accuracyrate of over 95% for objects within a 5 km range. The salient object extraction is achieved through a simplifiedconvolution kernel and the utilization of the object’s RGB features, which offer significantly faster computingspeeds compared to Convolutional Neural Networks (CNNs). Additionally, we introduce the dark channel beforethe fog removal algorithm, resulting in a 20 dB increase in image definition, which significantly benefits distanceestimation. Our system offers the advantages of stability and low device load, making it an asset for public securityaffairs and providing a reference point for future developments in surveillance hardware.

Automatic Miscalibration Detection and Correction of LiDAR and Camera Using Motion Cues

This paper aims to develop an automatic miscalibration detection and correction framework to maintain accurate calibration of LiDAR and camera for autonomous vehicle after the sensor drift.First,a monitoring algorithm that can continuously detect the miscalibration in each frame is designed,leveraging the rotational motion each individual sensor observes.Then,as sensor drift occurs,the projection constraints between visual feature points and LiDAR 3-D points are used to compute the scaled camera motion,which is further utilized to align the drifted LiDAR scan with the camera image.Finally,the proposed method is sufficiently compared with two representative approaches in the online experiments with varying levels of random drift,then the method is further extended to the offline calibration experiment and is demonstrated by a comparison with two existing benchmark methods.

Imaging simulation and analysis of attitude jitter effect on topographic mapping for lunar orbiter stereo optical cameras

The geometric accuracy of topographic mapping with high-resolution remote sensing images is inevita-bly affected by the orbiter attitude jitter.Therefore,it is necessary to conduct preliminary research on the stereo mapping camera equipped on lunar orbiter before launching.In this work,an imaging simulation method consid-ering the attitude jitter is presented.The impact analysis of different attitude jitter on terrain undulation is conduct-ed by simulating jitter at three attitude angles,respectively.The proposed simulation method is based on the rigor-ous sensor model,using the lunar digital elevation model(DEM)and orthoimage as reference data.The orbit and attitude of the lunar stereo mapping camera are simulated while considering the attitude jitter.Two-dimensional simulated stereo images are generated according to the position and attitude of the orbiter in a given orbit.Experi-mental analyses were conducted by the DEM with the simulated stereo image.The simulation imaging results demonstrate that the proposed method can ensure imaging efficiency without losing the accuracy of topographic mapping.The effect of attitude jitter on the stereo mapping accuracy of the simulated images was analyzed through a DEM comparison.

Anger-camera型中子探测器位置分辨性能研究

本文描述用于中国散裂中子源(CSNS)谱仪上,基于Anger-camera位置重建原理的闪烁体中子探测器样机位置分辨性能研究。在BL20中子测试束线上完成了探测器关键器件性能测试,包括硅光电倍增管(Silicon Photon Multiplier Tube, SiPM)阵列位置重建能力测试和耐辐照特性研究,同时对不同光导厚度下探测器位置分辨能力开展测试,发现基于SiPM阵列读出的Anger-camera型闪烁体中子探测器采用300μm的^(6)LiF/ZnS(Ag)闪烁屏和5 mm空气作为光导,其位置分辨最佳可达到0.6 mm。该探测器样机后期可通过大面积阵列拼接,实现中子衍射实验对大面积高位置分辨探测器的需求,其成功研制为加快谱仪建设提供有力的技术支撑。

Analyzing the Impact of Scene Transitions on Indoor Camera Localization through Scene Change Detection in Real-Time

Real-time indoor camera localization is a significant problem in indoor robot navigation and surveillance systems.The scene can change during the image sequence and plays a vital role in the localization performance of robotic applications in terms of accuracy and speed.This research proposed a real-time indoor camera localization system based on a recurrent neural network that detects scene change during the image sequence.An annotated image dataset trains the proposed system and predicts the camera pose in real-time.The system mainly improved the localization performance of indoor cameras by more accurately predicting the camera pose.It also recognizes the scene changes during the sequence and evaluates the effects of these changes.This system achieved high accuracy and real-time performance.The scene change detection process was performed using visual rhythm and the proposed recurrent deep architecture,which performed camera pose prediction and scene change impact evaluation.Overall,this study proposed a novel real-time localization system for indoor cameras that detects scene changes and shows how they affect localization performance.

Blackmagic Camera 8.5公测版更新发布

2023年12月,Blackmagic Camera8.5公测版正式发布。安装更新后,为Blackmagic URSA Broadcast G2添加了Blackmagic Cloud工作流程支持。可以从摄影机的新媒体池向Blackmagic Cloud上传片段,也可以直接上传到DaVinci Resolve项目。

Blackmagic Camera 8.5.1正式发布

针对Blackmagic URSA Broadcast G2的Blackmagic Camera 8.5.1版本,提供了Blackmagic Cloud流程支持,用户可从摄影机的新媒体池向Blackmagic Cloud上传片段,也可以直接上传到Da Vinci Resolve项目。新添加的同时代理记录功能加上Blackmagic RAW或ProRes能够助力影像迅速送进新闻演播室或剪辑室,提高工作流程的效率。

Pocket Cinema Camera系列和全画幅6K摄影机加入推流等新功能

Blackmagic Design最新发布的摄影机软件Blackmagic Camera 8.6公共测试版为Blackmagic Pocket Cinema Camera和Cinema Camera 6K机型增加了云流程支持,让用户可以将素材从摄影机媒体池上传到Blackmagic Cloud,甚至直接上传到DaVinci Resolve项目中。并为两款机型的USB-C接口增加了网络摄像头功能,即可把摄影机连接到电脑,并使用任何主流推流软件或平台以全高清1080P质量推流直播。

Blackmagic Camera iOS 2.1版软件更新

Blackmagic Camera iOS 2.1版软件更新,为Blackmagic Camera结合使用iPhone 16最新的相机控制功能添加了支持,可以通过单击或双击将其打开。还可以通过控制中心、锁定屏幕或操作按钮打开Blackmagic Camera,获得更多快速进入Blackmagic Camera并开始录制的方式。此外,可以使用iPhone 16内置的相机控制更轻松地导航显示并调整变焦、曝光、对焦、ISO和选择LUT等功能。

安卓版Blackmagic Camera发布

Blackmagic Design日前发布了安卓版Blackmagic Camera,将大幅改进拍摄效果,拍摄成果可用于影视制作。安卓版Blackmagic Camera采用和Blackmagic Design获奖摄影机一样的操作界面,直观易懂且对用户友好,可提供如同使用专业级数字电影摄影机一般的操控体验。通过简单的手指点按操作,就可以调整帧率、快门角度、白平衡以及ISO等设置。同时,还支持使用行业标准文件格式直接录制到Blackmagic Cloud,上至8K。