一种综合运动检测和视觉跟踪的智能监控系统

智能监控是当前计算机视觉领域中的热点问题之一。本文提出了一种运动检测与视觉跟踪相结合的智能监控系统,能自动完成轨迹的初始化和终止,能够对数目可变的目标进行自动跟踪。该系统首先利用基于颜色空间模型的阈值化背景减法提取出运动目标,然后结合基于MCMC的颜色粒子滤波器和全局最近邻法对多个目标进行跟踪。并基于OpenCV机器视觉库搭建了智能视频监控平台。实验表明,该系统可以实时有效地检测、跟踪数目变化不定的运动物体。     

基于智能视觉的车辆事故预警监控系统设计

针对车辆行驶中驾驶员视觉盲区或注意力不集中而造成的交通事故问题,提出采用智能视觉技术对车辆行驶过程中四周的异常物体进行视频监控,对动态视频场景中的运动目标进行检测、识别与实时测距,通过车辆智能视频监控系统最大程度的为驾驶员提供更多预警信息,预防交通事故的发生;文中介绍了车辆智能视觉监控系统的硬件设计方法与软件工作流程,并研究了运动目标检测算法与单目视觉测距算法,通过仿真实验,验证了该智能视觉车辆监控系统对于运动目标进行检测、识别与智能测距判断的实验结果.     

视觉关注转移的事件检测算法

智能监控系统已广泛应用于银行、超市、公交车等公共场合,监控视频的事件检测已经成为智能监控中的关键技术.提出了一种基于视觉关注转移的事件检测方法,该方法首先分别通过对视频帧进行动态和静态受关注模型的提取得到视觉关注显著图,然后根据视觉关注显著图的时域特性形成视觉关注节奏曲线,根据视觉关注节奏的变化强度选取关键帧,以关键帧形式表示受关注事件的发生.实验结果表明,算法提取的关键帧可以准确地标示监控视频中特征事件的发生,并且可以做到实时地检测事件.     

一种新的异常行为检测算法

行人异常行为的自动检测与识别是计算机视觉领域的重点和难点,同时也是智能监控系统中研究的热点问题。针对这一问题,提出了一种基于人体形态特征的异常检测算法。利用轮廓信息将目标从视频序列中分割出来,再对分割出来的目标进行轮廓拟合,根据所得到的拟合信息提取文中所定义的形态特征因子,将特征因子经过行为分类器的判定,从而决策出该行为是否异常。实验结果表明该方法实现简单,具有较好的实时性与鲁棒性,可以作为实时监控系统中异常行为检测的有效方法。     

基于视频分析的智能监控系统

智能监控是当前计算机视觉研究的热点领域之一。针对室内监控的具体特点,实现了一种基于视频分析的智能检测跟踪监控系统。利用基于统计模型的目标检测算法提取运动目标;然后结合Meanshift算法对目标进行粗跟踪;最后针对Meanshift算法无法实时改变跟踪窗大小的缺陷,提出边界力调整算法以自适应更新跟踪窗窗宽。并以DM642数字图像处理DSP为核心,设计并搭建了智能视频监控平台。实验表明,该系统可以实时有效地检测、跟踪室内运动目标。     

基于头部特征的人体检测

视觉监控是计算机视觉领域中近年来备受关注的前沿研究方向,视觉人体运动分析是视频监控中的研究重点。通过提取俯视图中人体头部区域特征,进行人体检测。由于人体头部区域的类圆形特征,一种圆形人体头部模型被提出,如何检测到合适的圆是人体检测的关键问题。本文采用对光照变化有较强鲁棒性的灰度梯度特征来检测人头。精确的实验表明了该算法在人体头部检测方面有较高的准确性和稳定性。     

智能化监控系统信息处理研究

智能视觉监视技术在公共保安领域中的应用带来了监控系统的巨大变革。现在可以把人体识别引入到监控系统中,设计能完全替代监控人员的、可用于无人值守情况下的高智能化监控系统。本文详细介绍了人体运动的视觉监视技术的智能化监控系统一般遵从的处理流程,包括运动检测、人体识别和目标跟踪。     

智能视频监控系统结构分析

视频智能监控系统是一套运用于公共场合,能够实现实时记录与数据分析。该系统可以实现普通视频监控系统中的视频数据记录功能,并且解决了人为视频监控中无法实时判断特征信息的功能缺陷。     

智能视觉监控技术研究进展

新一代智能视觉监控技术的研究是一个极具挑战性的前沿课题,它旨在赋予监控系统观察分析场景内容的能力,实现监控的自动化和智能化,因而具有巨大的应用潜力。视觉监控系统的智能化分析过程由运动目标检测、分类、跟踪和视频内容分析等几个基本环节组成,其中视频内容分析又包括异常检测、人的身份识别以及视频内容理解描述等。本文在总结以上有关关键技术研究进展的基础上,进一步提出将超分辨率复原技术引入视觉监控领域,介绍了超分辨率复原的主要算法及其在智能视觉监控中的应用。     

基于Matlab实时运动目标跟踪检测系统

运动目标的检测跟踪是视频理解技术和计算机视觉的研究热点,其在解决智能视频监控,人机交互,智能交通系统等领域有着广泛而重要的应用,基于此利用matlab平台构建出USB摄像头实时图像采集处理系,从视频流采集到处理综合利用了背景估测,图像分割,目标检测与跟踪算法准确高效地检测出环境场景中的动态目标,并成功地对其进行实时追踪。利用matlab的simulink模块编程实现提取视频流YcbCr输入系统进行运算处理,并改进了背景估测和目标检测算法,提高系统的实时性。最终利用多次试验,对室内和室外运动目标实现检测跟踪,验证了系统处理实际问题的可靠性能。     

Trajectory-based vehicle tracking at low frame rates

In smart cities, an intelligent traffic surveillance system plays a crucial role in reducing traffic jams and air pollution, thus improving the quality of life. An intelligent traffic surveillance should be able to detect and track multiple vehicles in real-time using only limited resources. Conventional tracking methods usually run at a high video-sampling rate, assuming that the same vehicles in successive frames are similar and move only slightly. However, in cost effective embedded surveillance systems (e.g., a distributed wireless network of smart cameras), video frame rates are typically low because of limited system resources. Therefore, conventional tracking methods perform poorly in embedded surveillance systems because of discontinuity of the moving vehicles in the captured recordings. In this study, we present a fast and light algorithm that is suitable for an embedded real-time visual surveillance system to detect effectively and track multiple moving vehicles whose appearance and/or position changes abruptly at a low frame rate. For effective tracking at low frame rates, we propose a new matching criterion based on greedy data association using appearance and position similarities between detections and trackers. To manage abrupt appearance changes, manifold learning is used to calculate appearance similarity. To manage abrupt changes in motion, the next probable centroid area of the tracker is predicted using trajectory information. The position similarity is then calculated based on the predicted next position and progress direction of the tracker. The proposed method demonstrates efficient tracking performance during rapid feature changes and is tested on an embedded platform (ARM with DSP-based system).     

A flexible communications protocol for a distributed surveillance system

On-line surveillance for safety and security is a major requirement of public transport and other public places to address the modern demands of mobility in major urban areas and to effect improvements in quality of life and environment protection. The surveillance task is a complex one involving technology, management procedures and people. Visual surveillance based on Closed Circuit Television system is an important part of such systems, but visual processing is not sufficient and the geographical distribution of devices and management has to be taken into account. In this paper we present a surveillance architecture that reflects the distributed nature of the monitoring task and allows for distributed detection processes, not only dealing with visual processing but also with devices such as acoustic signature detection and mobile smart cards, actuators and a range of other possible sensors. The design uses ideas from control engineering and distributed communications networks resulting in a communications architecture based on CORBA and XML messaging. We have shown how to define a generic device/sensor model appropriate for the surveillance task and sufficiently flexible so as to allow for scalability, expansion and customisation of a practical surveillance task. The paper gives sufficient details on the protocols to show how intelligent detection modules can be integrated as part of this kind of system. The system components have been implemented and integrated in two major successful trials in metropolitan railway stations in London and in Paris, as part of a major EU-funded project (PRISMATICA).     

A real-time object detecting and tracking system for outdoor night surveillance

Autonomous video surveillance and monitoring has a rich history. Many deployed systems are able to reliably track human motion in indoor and controlled outdoor environments. However, object detection and tracking at night remain very important problems for visual surveillance. The objects are often distant, small and their signatures have low contrast against the background. Traditional methods based on the analysis of the difference between successive frames and a background frame will do not work. In this paper, a novel real time object detection algorithm is proposed for night-time visual surveillance. The algorithm is based on contrast analysis. In the first stage, the contrast in local change over time is used to detect potential moving objects. Then motion prediction and spatial nearest neighbor data association are used to suppress false alarms. Experiments on real scenes show that the algorithm is effective for night-time object detection and tracking.     

Abnormal visual event detection based on multi-instance learning and autoregressive integrated moving average model in edge-based Smart City surveillance

The abnormal visual event detection is an important subject in Smart City surveillance where a lot of data can be processed locally in edge computing environment. Real-time and detection effectiveness are critical in such an edge environment. In this paper, we propose an abnormal event detection approach based on multi-instance learning and autoregressive integrated moving average model for video surveillance of crowded scenes in urban public places, focusing on real-time and detection effectiveness. We propose an unsupervised method for abnormal event detection by combining multi-instance visual feature selection and the autoregressive integrated moving average model. In the proposed method, each video clip is modeled as a visual feature bag containing several subvideo clips, each of which is regarded as an instance. The time-transform characteristics of the optical flow characteristics within each subvideo clip are considered as a visual feature instance, and time-series modeling is carried out for multiple visual feature instances related to all subvideo clips in a surveillance video clip. The abnormal events in each surveillance video clip are detected using the multi-instance fusion method. This approach is verified on publically available urban surveillance video datasets and compared with state-of-the-art alternatives. Experimental results demonstrate that the proposed method has better abnormal event detection performance for crowded scene of urban public places with an edge environment.     

Audio-Visual Event Recognition in Surveillance Video Sequences

In the context of the automated surveillance field, automatic scene analysis and understanding systems typically consider only visual information, whereas other modalities, such as audio, are typically disregarded. This paper presents a new method able to integrate audio and visual information for scene analysis in a typical surveillance scenario, using only one camera and one monaural microphone. Visual information is analyzed by a standard visual background/foreground (BG/FG) modelling module, enhanced with a novelty detection stage and coupled with an audio BG/FG modelling scheme. These processes permit one to detect separate audio and visual patterns representing unusual unimodal events in a scene. The integration of audio and visual data is subsequently performed by exploiting the concept of synchrony between such events. The audio-visual (AV) association is carried out online and without need for training sequences, and is actually based on the computation of a characteristic feature called audio-video concurrence matrix, allowing one to detect and segment AV events, as well as to discriminate between them. Experimental tests involving classification and clustering of events show all the potentialities of the proposed approach, also in comparison with the results obtained by employing the single modalities and without considering the synchrony issue     

基于FPGA的运动目标远程监视系统设计

运动目标检测是智能安防系统的重要组成部分,为了满足安防系统远距离监视目标以及视频传输实时性等需求,设计了一种基于FPGA平台的运动目标远程监视系统;该系统以Xilinx公司的Artix7系列FPGA芯片为核心,通过OV5640摄像头实现视频图像的采集,将采集到的图像进行灰度化处理,并通过DDR3存储器缓存处理后的图像,采用帧间差分法运动目标检测技术实现对多个运动物体的检测与标记,将检测结果通过以太网的UDP协议传输到上位机实时显示;实验结果表明,在图像分辨率为640*480时,以太网UDP传输速度为133Mbit/s,视频图像帧率为26fps,大于人眼的可视帧率24fps,满足视频传输实时性的要求,同时该系统能够远距离、高效地检测与跟踪多个运动目标,相比于其他系统具有可远程实时检测、小型化、低功耗的特点,可进一步应用到智能安防系统中。     

基于快速级联分类器的行人检测方法研究

行人检测是图像处理、计算机视觉等方面研究的重要环节,通常用于视频监控和智能车辆等领域。行人检测图像易受到背景的影响,常用的帧差法及单纯训练分类器法在行人检测中存在着准确率低、分类训练算法复杂、实时性差等问题。首先采用改进型帧差法获取行人运动信息,然后利用直方图坐标对应划分出运动区域,最后通过训练双特征级联分类器对运动区域进行检测识别。实验结果表明,本方法可以有效减少误检和漏检现象,检测时间平均减少了32.77ms,检测准确率平均提高了10%以上,因此本方法有效提高了识别准确率和识别速度。     

Suspicious human activity recognition: a review

Suspicious human activity recognition from surveillance video is an active research area of image processing and computer vision. Through the visual surveillance, human activities can be monitored in sensitive and public areas such as bus stations, railway stations, airports, banks, shopping malls, school and colleges, parking lots, roads, etc. to prevent terrorism, theft, accidents and illegal parking, vandalism, fighting, chain snatching, crime and other suspicious activities. It is very difficult to watch public places continuously, therefore an intelligent video surveillance is required that can monitor the human activities in real-time and categorize them as usual and unusual activities; and can generate an alert. Recent decade witnessed a good number of publications in the field of visual surveillance to recognize the abnormal activities. Furthermore, a few surveys can be seen in the literature for the different abnormal activities recognition; but none of them have addressed different abnormal activities in a review. In this paper, we present the state-of-the-art which demonstrates the overall progress of suspicious activity recognition from the surveillance videos in the last decade. We include a brief introduction of the suspicious human activity recognition with its issues and challenges. This paper consists of six abnormal activities such as abandoned object detection, theft detection, fall detection, accidents and illegal parking detection on road, violence activity detection, and fire detection. In general, we have discussed all the steps those have been followed to recognize the human activity from the surveillance videos in the literature; such as foreground object extraction, object detection based on tracking or non-tracking methods, feature extraction, classification; activity analysis and recognition. The objective of this paper is to provide the literature review of six different suspicious activity recognition systems with its general framework to the researchers of this field.