Appearance based background subtraction for PTZ cameras

Traditional background subtraction algorithms assume the camera is static and are based on simple per-pixel models of scene appearance. This leads to false detections when the camera moves. While this can sometimes be addressed by online image registration, this approach is prone to dramatic failures and long-term drift. We present a novel background subtraction algorithm designed for pan-tilt-zoom cameras that overcomes this challenge without the need for explicit image registration. The proposed algorithm automatically trains a discriminative background model, which is global in the sense that it is the same regardless of image location. Our approach first extracts multiple features from across the image and uses principal component analysis for dimensionality reduction. The extracted features are then grouped to form a Bag of Features. A global background model is then learned from the bagged features using Support Vector Machine. The proposed approach is fast and accurate. Having a single global model makes it computationally inexpensive in comparison to traditional pixel-wise models. It outperforms several state-of-the-art algorithms on the CDnet 2014 pan-tilt-zoom and baseline categories and Hopkins155 dataset. In particular, it achieves an F-Measure of 75.41% on the CDnet dataset PTZ category, significantly better than the previously reported best score of 62.07%. These results show that by removing the coupling between detection model and spatial location, we significantly increase the robustness to camera motion.     

实时背景噪声补偿的激光雷达能见度仪设计

基于实时背景噪声补偿的方法,设计了一台激光雷达能见度仪。该系统采用了模块化的设计思想,以嵌入式计算机PCM-3370作为系统的控制和数据处理单元,通过门控电路控制系统各模块的工作时序。针对强背景光照条件下信噪比恶化的实际情况,采用了一种新的实时背景噪声补偿与采用阈值分割的移动平均法相结合的方法处理信号,大幅度地提高了信噪比。最后通过数值仿真和外场对比试验,验证了本设计的准确性和有效性。     

一种基于背景减法的运动检测算法评价方法

利用传统的运动检测算法评价方法的原理,提出一种适用于背景减法的运动检测算法的性能评价方法.该方法基于理想的检测对象可以获得(可以通过人工方法获得,或通过某种可靠的途径获得).现实现了W4和W4+算法的C+ +代码,并用提出的评价方法对其进行评价,针对风动树叶背景的视频图像,识别运动物体.实验结果表明,该评价方法可以量化地表明运动检测算法检测效果的优劣.     

基于运动目标的快速背景建模算法

针对智能交通车流量检测系统,提出了一种适用于嵌入式系统的快速轻量背景建模方法.该方法先由帧差法过滤视频序列,抽取运动物体少的帧进行存储,再利用改进的高斯模型快速学习获得基础模型,并结合帧差和像素统计的方法对背景模型进行自适应更新,对传统混合高斯模型的缺陷进行了改善,在基于 TMS320DM648的图像处理客户端上表现出较好的实时性和天气适应性,以及更高的处理效能.     

基于ZYNQ的人脸检测的快速实现

本文是在ZYNQ平台上,结合加入阈值处理的背景减除法实现人脸检测的快速实现。ZYNQ是ARM+FPGA的体系架构,本文利用该芯片的PL（FPGA）端来实现图像的预处理;利用PS（ARM）端对来自PL端的图像利用背景减除法进行前景的提取,然后对前景图片进行人脸检测。最终,本文实现了人脸检测的时间在ZYNQ平台上平均减少了700ms以上,而且在识别率方面也有所提高。     

一种实时可靠的视频交通流量检测方法

讨论了一种基于视频的交通流量统计方法,提出了相应的算法.采用叠加平均的方法从一系列图像序列中提取出静止背景,并通过减背景的方法提取出运动车辆并对其进行相应的处理,然后用聚类分析的方法对其进行车辆计数分析,并对背景进行自适应更新.实验证明该方法具有较好的性能,满足实时性处理的要求,稳定性较高.     

3D saliency detection based on background detection

Unlike 2D saliency detection, 3D saliency detection can consider the effects of depth and binocular parallax. In this paper, we propose a 3D saliency detection approach based on background detection via depth information. With the aid of the synergism between a color image and the corresponding depth map, our approach can detect the distant background and surfaces with gradual changes in depth. We then use the detected background to predict the potential characteristics of the background regions that are occluded by foreground objects through polynomial fitting; this step imitates the human imagination/envisioning process. Finally, a saliency map is obtained based on the contrast between the foreground objects and the potential background. We compare our approach with 14 state-of-the-art saliency detection methods on three publicly available databases. The proposed model demonstrates good performance and succeeds in detecting and removing backgrounds and surfaces of gradually varying depth on all tested databases.     

Detection system based on a novel large area hybrid detector

A system level implementation of a large area hybrid detector is presented. The detector used in this system consists of an array of hydrogenated amorphous silicon photodiodes directly connected to a CMOS readout chip, which is vertically integrated over the sensor array using flip-chip bonding. In particular, the proposed solution relies on a stack of interconnection layers, deposited on top of the photodiode array, to route each individual pixel output to a separate pre-amplifier channel. This avoids the need for a geometrical matching between the sensor array and the chip contact pads. As a consequence, conventional non-pixelated readout chip can be used and easy-scalable large area detectors can be produced. The CMOS chip is connected to an electronic board, providing the interfaces needed to read the signals as well as providing voltage references and power to the chip. The signals are collected and pre-processed by an FPGA chip, providing a very compact and flexible setup.     

基于混合高斯模型的运动车辆检测方法

针对目前在车辆检测中广泛应用的混合高斯模型(G MM)存在的缺陷,提出了一种改进的GMM运 动车辆检测方法。对于GMM运行过程中“鬼影”长期存在的缺陷,通过采用新 的权值和方差更新方 法,加速“鬼影”的消除,改善其车辆检测性能;对于传统的GMM对所有像素 点均采用固定分布 数建模造成的内存空间浪费,通过设定一个分布数上限值,对未达到上限值的像素 点采用分布数 自适应变化的方法,有效地减少模型总分布数,节约内存空间。实验结果表明,改进后的GM M在“鬼影”的消除和计算速度上具有较大的优势。     

基于像素与子块的背景建模级联算法

针对子块级背景建模方法无法保证所提取前景形状的精确性及像素级背景建模方法无法有效处理非平稳场景的问题,提出了一种背景建模分层模型,首先采用文中子块级建模算法得到较为粗糙的背景区域和前景区域,然后利用混合高斯模型对特定图像区域执行像素级的前景提纯或背景模型更新操作,2种不同层次的算法通过非对称前向反馈机制进行级联。实验结果表明,所提分层模型在能够有效处理非平稳场景的同时保证了所提取前景形状的精确性,且对光照突变不敏感,建模效果优于级联算法中任一独立算法,而处理时间小于2种独立算法处理时间之和,满足了实时处理要求。     

基于融合差分的可变光强自适应目标识别算法

主要研究视频监控系统中运动目标检测算法,提出一种背景差分与帧间差分相融合的方法。该算法通过多次差分以及判决区域的相关运算划定背景区域和运动区域。同时参考相邻帧平均灰度信息更新背景帧以适应光线变化对判断造成的影响。在图像后处理中结合相关形态学算划分最终的运动目标。该算法可实现运动目标的快速准确定位和区域估算,实验表明该算法的时间复杂度和空间复杂度低,效果良好。     

一种基于机器视觉的视频交通流检测系统

简要分析了交通流检测技术的发展现状,结合当前智能交通系统的应用需求,利用连续三帧差分的运动估计方法来构建初始背景,并采用统计打分的策略实时地对背景进行更新;同时提出了一种简单而有效的阴影消除算法以提高交通流参数检测的准确度。另外,针对现有交通流检测系统无车辆跟踪这一环节,可能导致流量多计数的问题,本文提出同时利用车辆的位置信息、颜色信息和分形维信息对车辆进行匹配跟踪的策略。大量实验证明该检测算法能快速、有效地检测出各种交通流参数,为实现交通管理的自动化奠定基础。     

改进特征空间的红外弱小目标背景建模法

为有效去除动态背景对弱小目标信号的干扰,提出改进特征空间的红外弱小目标背景建模法来抑制背景。先采用改进的各向异性滤波算法从空域角度进行滤波以约束图像各个组分的差异,紧接着取连续时间域上多帧滤波后的图像组成一个特征矩阵,借助于主成分分析法进行特征分解,最后将输入图像投影到特征空间上进行背景建模,同时为了适应动态变化的背景,在时域上以一定学习率来更新背景模型。实验结果表明,提出的算法比传统的算法取得更好的背景估计效果,结构相似性SSIM、对比度增益I和背景抑制因子BIF分别大于0.97、15.46和5.25。     

基于混合算法的堆垛机实时调度建模优化研究

堆垛机任务调度是自动化立体仓库提高作业效率、实现智能控制的核心要素之一。首先建立堆垛机实时调度系统的任务指令模型,进而将模型求解抽象为作业路径长度最短的旅行商TSP问题。利用遗传—退火混合算法解决TSP问题,根据混合算法特点设计算法流程,运用数学工具进行任务调度方案仿真,结果证明遗传—退火混合算法比单一的遗传算法或模拟退火算法在最优路径求解和时间耗费上具有优越性。     

基于背景差分的一种运动目标检测方法

针对运动目标检测中,传统背景差分法在运动目标和背景颜色相近时不易检测的缺点,提出了一种检测完整运动目标的方法。该方法对YUV彩色空间下的3个通道分别选取独立的阈值进行初次检测,最大化地利用了视频中图像的色彩信息。在包含初次检测所获运动目标的最小矩形区域内进行二次检测,有效地提高了检测精度。实验证明,相比于常规方法,该方法的检测结果更加清晰完整。     

Depth sensor assisted real-time gesture recognition for interactive presentation

In this paper, we present a gesture recognition approach to enable real-time manipulating projection content through detecting and recognizing speakers gestures from the depth maps captured by a depth sensor. To overcome the limited measurement accuracy of depth sensor, a robust background subtraction method is proposed for effective human body segmentation and a distance map is adopted to detect human hands. Potential Active Region (PAR) is utilized to ensure the generation of valid hand trajectory to avoid extra computational cost on the recognition of meaningless gestures and three different detection modes are designed for complexity reduction. The detected hand trajectory is temporally segmented into a series of movements, which are represented as Motion History Images. A set-based soft discriminative model is proposed to recognize gestures from these movements. The proposed approach is evaluated on our dataset and performs efficiently and robustly with 90% accuracy.     

Semi-automatic 2D-to-3D video conversion based on background sprite generation

This paper presents a technique for semi-automatic 2D-to-3D stereo video conversion, which is known to provide user intervention in assigning foreground/background depths for key frames and then get depth maps for non-key frames via automatic depth propagation. Our algorithm treats foreground and background separately. For foregrounds, kernel pixels are identified and then used as the seeds for graph-cut segmentation for each non-key frame independently, resulting in results not limited by objects’ motion activity. For backgrounds, all video frames, after foregrounds being removed, are integrated into a common background sprite model (BSM) based on a relay-frame-based image registration algorithm. Users can then draw background depths for BSM in an integrated manner, thus reducing human efforts significantly. Experimental results show that our method is capable of retaining more faithful foreground depth boundaries (by 1.6–2.7 dB) and smoother background depths than prior works. This advantage is helpful for 3D display and 3D perception.     

Background memory-assisted zero-shot video object segmentation for unmanned aerial and ground vehicles

Unmanned aerial vehicles (UAV) and ground vehicles (UGV) require advanced video analytics for various tasks, such as moving object detection and segmentation; this has led to increasing demands for these methods. We propose a zero-shot video object segmentation method specifically designed for UAV and UGV applications that focuses on the discovery of moving objects in challenging scenarios. This method employs a background memory model that enables training from sparse annotations along the time axis, utilizing temporal modeling of the background to detect moving objects effectively. The proposed method addresses the limitations of the existing state-of-the-art methods for detecting salient objects within images, regardless of their movements. In particular, our method achieved mean $J$ and $F$ values of 82.7 and 81.2 on the DAVIS'16, respectively. We also conducted extensive ablation studies that highlighted the contributions of various input compositions and combinations of datasets used for training. In future developments, we will integrate the proposed method with additional systems, such as tracking and obstacle avoidance functionalities.     

基于改进单模高斯模型的运动目标检测算法

在视频交通车辆目标检测中,阴影问题是影响其检测准确性的关键问题之一.为了解决这个问题,提出了一种结合单模高斯模型和背景差法的运动目标阴影检测方法.首先针对传统单模高斯模型提出了一种自适应学习率和选择性差值更新背景相结合的方法,加快了背景模型的初始化速度,同时结合背景差法对阴影部分进行检测与去除.实验结果表明,该方法能够较好地去除车辆的阴影,提高了检测的准确性.     

基于最小二阶导数的低复杂度视频背景建模算法

为了训练出适应于视频压缩域的高质量背景模型,该文根据像素点在时域上的分布特征,提 出一种基于最小二阶导数的低复杂度视频背景建模算法。首先,根据函数 的二阶导数性质来判断其波动特性;然后,通过二次差分拟合像素点在时域上的二阶导数, 得到各个像素点的稳定 性;最后,根据设定阈值分离出各个位置的像素点在训练周期内最平稳的值,将其作为相应 位置的背景模型值。实 验结果显示,与AVS2相比,BD-rate节省了9.83%,BD- PSNR提升了 0.37 dB。与AVS2-S的背景建模算法相比, 本算法有效改善了前景污染问题,降低了算法复杂度。