利用视频图像检测车位状态的方法

为了实现停车场空车位的快速自动寻找,提出了一种用于视频图像车位检测的车位组合状态网络模型,在最大后验概率意义下,求解最可能的车位状态.对改进的HSI车位图像像素进行贝叶斯分类,利用预先训练好的条件概率计算出相邻车位状态传递概率,通过车位组合状态网络的优化,确定出最佳的车位状态.该方法可以很好地解决车辆遮挡所造成的误判问题,准确地检出车位的占有情况.实验结果表明,该方法具有较高的准确性与鲁棒性,特别是可以较好地消除车辆相互遮挡和环境光线变化对检测结果造成的影响.     

Detection of abnormal traffic video images based on high-dimensional fuzzy geometry

Detection of abnormal video images of transportation is to find out video images that contain abnormities among all images of transportation using video and image processing and analyzing techniques. It is an important component of intelligent transportation system, which can not only reduce the workload of traffic managers, but also effectively improve the efficiency of traffic management. However, video images of transportation in practice usually have complex backgrounds, and current detecting algorithms of traffic abnormity sometimes become ineffective due to interference factors such as noises and affine transformation (illumination variation, target occlusion, scale changes and view changes, etc.). In order to overcome these interference factors and fuzzy uncertainties in image representation, as well as improve the accuracy of traffic images representation, this study explored the representation methods of traffic images using fuzzy geometry theory on the basis of fuzzy uncertainties occurring during the process of imaging, transmission and processing of images; moreover, this study also put forward two kinds of representation algorithms of traffic images, and analyzed and verified effectiveness of representation algorithms based on theories and experiments.     

Face tracking and recognition in video moving images based on convolutional neural network

Multimedia Tools and Applications - In order to accurately track and recognize faces in video moving images, a method for tracking and recognizing faces in video moving images based on...     

Detection of crowdedness in bus compartments based on ResNet algorithm and video images

Multimedia Tools and Applications - The crowding in bus is an important factor affecting passenger satisfaction and bus dispatching level. However, how to use video images to detect crowding...     

Mobile manipulation: The robotic assistant

Mobile manipulation capabilities are key to many new applications of robotics in space, underwater, construction, and service environments. This paper discusses the development of robotic “assistance” capabilities to aid workers in the accomplishment of a variety of physical operations and presents various control strategies developed for vehicle-arm coordination, compliant motion tasks, and cooperative manipulation between multiple platforms. These strategies have been implemented on two holonomic mobile platforms designed and built at Stanford in collaboration with Oak Ridge National Laboratories and Nomadic Technologies.     

Detection of passenger flow on and off buses based on video images and YOLO algorithm

Bus passenger flow information is very important as a reference data for bus company line optimization, schedule scheduling basis, and passenger travel mode arrangement. With the development of image processing technology, it has become a current research trend to count passenger flow with the help of surveillance video of passengers getting on and off the bus. The specific research contents of this paper based on video image detection and statistics of passengers are as follows:(1) Collect head target image samples through a variety of ways, including 3960 positive head target samples and 4150 negative head target samples, which together constitute the head target feature database. (2) Established a head target detection model based on deep learning. First, the labeling of the head target training data set is completed. Then, after 15,000 iterations of model training, the YOLOv3 head target detection network model was obtained, with a recall rate of 92.12% and an accuracy rate of 89.71%. (3) A multi-target matching tracking algorithm based on the combination of Cam-shift and YOLOv3 is proposed. First, the Cam-shift algorithm is used to track the head target. Secondly, the head target tracking data and the YOLOv3 detection data are combined to solve the problem of drift during the tracking of the Cam-shift algorithm through the data association matching method based on the minimum distance, and then combined with the time constraint, a passenger location information judgment rule is proposed. Optimize the error and missed detection in the process of head target detection and tracking, and improve the reliability of passenger trajectory tracking. (4) A statistical algorithm for the detection of passengers getting on and off the bus is proposed. First, the trajectory of passengers in the bus boarding and disembarking area is analyzed, and a process for judging passengers’ boarding and boarding behavior is proposed. At the same time, a passenger position information judgment rule is proposed according to the different situations of whether there are new passengers or missing passengers, so as to optimize the problem of wrong detection and missing detection in the process of head target detection and tracking. (5) Finally, experiments are carried out in actual bus scenes and simulation scenes. The experiment proves that the statistical algorithm for the detection of passengers getting on and off the bus proposed in this paper has good detection, tracking and statistics effects in bus scenes and simulation scenes.

     

基于LVQ神经网络的视频监控图像火焰检测与识别算法

针对传统火灾火焰探测技术存在不稳定、误判率高的缺点,提出了一种基于人工神经网络的火焰检测与识别算法。通过分析火焰图像的动态特性,利用火焰图像序列的离心率、放射性和整体移动等特征信息,结合学习向量量化(LVQ)神经网络进行训练仿真。实验结果表明,该算法能有效提高监控视频图像中可疑火焰的快速分类,稳定性强,具有较高的火焰识别准确率。     

An automatic 2D to 3D video conversion approach based on RGB-D images

3D movies/videos have become increasingly popular in the market; however, they are usually produced by professionals. This paper presents a new technique for the automatic conversion of 2D to 3D video based on RGB-D sensors, which can be easily conducted by ordinary users. To generate a 3D image, one approach is to combine the original 2D color image and its corresponding depth map together to perform depth image-based rendering (DIBR). An RGB-D sensor is one of the inexpensive ways to capture an image and its corresponding depth map. The quality of the depth map and the DIBR algorithm are crucial to this process. Our approach is twofold. First, the depth maps captured directly by RGB-D sensors are generally of poor quality because there are many regions missing depth information, especially near the edges of objects. This paper proposes a new RGB-D sensor based depth map inpainting method that divides the regions with missing depths into interior holes and border holes. Different schemes are used to inpaint the different types of holes. Second, an improved hole filling approach for DIBR is proposed to synthesize the 3D images by using the corresponding color images and the inpainted depth maps. Extensive experiments were conducted on different evaluation datasets. The results show the effectiveness of our method.

     

Information based indoor environment robotic exploration and modeling using 2-D images and graphs

As the autonomy of personal service robotic systems increases so has their need to interact with their environment. The most basic interaction a robotic agent may have with its environment is to sense and navigate through it. For many applications it is not usually practical to provide robots in advance with valid geometric models of their environment. The robot will need to create these models by moving around and sensing the environment, while minimizing the complexity of the required sensing hardware. Here, an information-based iterative algorithm is proposed to plan the robot's visual exploration strategy, enabling it to most efficiently build a graph model of its environment. The algorithm is based on determining the information present in sub-regions of a 2-D panoramic image of the environment from the robot's current location using a single camera fixed on the mobile robot. Using a metric based on Shannon's information theory, the algorithm determines potential locations of nodes from which to further image the environment. Using a feature tracking process, the algorithm helps navigate the robot to each new node, where the imaging process is repeated. A Mellin transform and tracking process is used to guide the robot back to a previous node. This imaging, evaluation, branching and retracing its steps continues until the robot has mapped the environment to a pre-specified level of detail. The set of nodes and the images taken at each node are combined into a graph to model the environment. By tracing its path from node to node, a service robot can navigate around its environment. This method is particularly well suited for flat-floored environments. Experimental results show the effectiveness of this algorithm.     

Automated crop plant detection based on the fusion of color and depth images for robotic weed control

Robotic weeding enables weed control near or within crop rows automatically, precisely and effectively. A computer‐vision system was developed for detecting crop plants at different growth stages for robotic weed control. Fusion of color images and depth images was investigated as a means of enhancing the detection accuracy of crop plants under conditions of high weed population. In‐field images of broccoli and lettuce were acquired 3–27 days after transplanting with a Kinect v2 sensor. The image processing pipeline included data preprocessing, vegetation pixel segmentation, plant extraction, feature extraction, feature‐based localization refinement, and crop plant classification. For the detection of broccoli and lettuce, the color‐depth fusion algorithm produced high true‐positive detection rates (91.7% and 90.8%, respectively) and low average false discovery rates (1.1% and 4.0%, respectively). Mean absolute localization errors of the crop plant stems were 26.8 and 7.4 mm for broccoli and lettuce, respectively. The fusion of color and depth was proved beneficial to the segmentation of crop plants from background, which improved the average segmentation success rates from 87.2% (depth‐based) and 76.4% (color‐based) to 96.6% for broccoli, and from 74.2% (depth‐based) and 81.2% (color‐based) to 92.4% for lettuce, respectively. The fusion‐based algorithm had reduced performance in detecting crop plants at early growth stages.     

Stereoscopic video images for telerobotic applications

This article addresses the use of stereoscopic images in teleoperated tasks. Depth perception is a key point in the ability to skillfully manipulate in remote environments. Displaying three‐dimensional images is a complex process but it is possible to design a teleoperation interface that displays stereoscopic images to assist in manipulation tasks. The appropriate interface for image viewing must be chosen and the stereoscopic video cameras must be calibrated so that the image disparity is natural for the observer. Attention is given to the calculation of stereoscopic image disparity, and suggestions are made as to the limits within which adequate stereoscopic image perception takes place. The authors have designed equipment for image visualization in teleoperated systems. These devices are described and their performance evaluated. Finally, an architecture for the transmission of stereoscopic video images via network is proposed, which in the future will substitute for current image processing devices. © 2005 Wiley Periodicals, Inc.     

Cognitive psychological analysis based on multilayer semantics of web video and feature extraction of psychological images

Multimedia Tools and Applications - In order to improve the accuracy of psychological analysis by extracting network video and image features, it is difficult to analyze video semantics and blur...     

Spatial-temporal joint probability images for video segmentation

Effective annotation and content-based search for videos in a digital library require a preprocessing step of detecting, locating and classifying scene transitions, i.e., temporal video segmentation. This paper proposes a novel approach—spatial-temporal joint probability image (ST-JPI) analysis for temporal video segmentation. A joint probability image (JPI) is derived from the joint probabilities of intensity values of corresponding points in two images. The ST-JPT, which is a series of JPIs derived from consecutive video frames, presents the evolution of the intensity joint probabilities in a video. The evolution in a ST-JPI during various transitions falls into one of several well-defined linear patterns. Based on the patterns in a ST-JPI, our algorithm detects and classifies video transitions effectively.Our study shows that temporal video segmentation based on ST-JPIs is distinguished from previous methods in the following way: (1) It is effective and relatively robust not only for video cuts but also for gradual transitions; (2) It classifies transitions on the basis of predefined evolution patterns of ST-JPIs during transitions; (3) It is efficient, scalable and suitable for real-time video segmentation. Theoretical analysis and experimental results of our method are presented to illustrate its efficacy and efficiency.     

Quality analysis of MPEG-4 video images

A method for analyzing the quality of reproduction of fine details in color video images, on the basis of objective criteria is proposed. The results of experimental testing of video images in the MPEG-4 visual standard by a program analyzer are given.     

Real-time detection of steam in video images

In this paper, we present a real-time image processing technique for the detection of steam in video images. The assumption made is that the presence of steam acts as a blurring process, which changes the local texture pattern of an image while reducing the amount of details. The problem of detecting steam is treated as a supervised pattern recognition problem. A statistical hidden Markov tree (HMT) model derived from the coefficients of the dual-tree complex wavelet transform (DT-CWT) in small 48×48 local regions of the image frames is used to characterize the steam texture pattern. The parameters of the HMT model are used as an input feature vector to a support vector machine (SVM) technique, specially tailored for this purpose. By detecting and determining the total area covered by steam in a video frame, a computerized image processing system can automatically decide if the frame can be used for further analysis. The proposed method was quantitatively evaluated by using a labelled image data set with video frames sampled from a real oil sand video stream. The classification results were 90% correct when compared to human labelled image frames. The technique is useful as a pre-processing step in automated image processing systems.     

Hemispherical density functions applied to video images

Fish-eye photographic lenses which project a hemispherical object region onto a circular image plane are often used to capture complex geometrics of radiating environments. Manual methods are currently used to analyse the resulting photographs. This paper uses hemispherical density functions to develop algorithms which provide for the automated analysis of digitised fish-eye lens images, thus making feasible the analysis of large data sets of such images.     

基于FPGA的交通视频快速去雾系统的设计与实现

针对雾天交通监控视频图像退化问题,提出了一种基于FPGA架构的雾天交通视频图像快速去雾系统。首先将采集到的实时图像数据缓存到SDRAM中,然后在亮度分量基础上估计传播图,最后基于大气散射模型复原清晰图像。该系统利用FPGA并行运算处理能力强、逻辑资源丰富等特性,针对PAL制式640×480彩色图像,处理速度为60帧/s。实验结果表明,该系统在保证输出视频质量的前提下达到了很好的去雾效果。     

Efficient video encryption scheme based on advanced video coding

A video encryption scheme combining with advanced video coding (AVC) is presented and analyzed in this paper, which is different from the ones used in MPEG1/2 video encryption. In the proposed scheme, the intra-prediction mode and motion vector difference are encrypted with the length-kept encryption algorithm (LKE) in order to keep the format compliance, and the residue data of the macroblocks are encrypted with the residue data encryption algorithm (RDE) in order to keep low cost. Additionally, a key distribution scheme is proposed to keep the robustness to transmission errors, which assigns sub-keys to different frames or slices independently. The encryption scheme’s security, time efficiency and error robustness are analyzed in detail. Experimental results show that the encryption scheme keeps file format unchanged, is secure against replacement attacks, is efficient in computing, and is robust to some transmission errors. These properties make it a suitable choice for real-time applications, such as secure IPTV, secure videoconference or mobile/wireless multimedia, etc.

基于视频的角色动画

现有的计算机动画制作普遍存在投资大、效率不高等缺陷.为此,提出了利用计算机视觉技术中的一种基于特征的跟踪方法-KL(Kanade-Lucas)算法.首先捕获视频中角色的二维运动信息,然后进行三维重建生成满足动画要求的三维角色运动序列,最后将三维角色运动序列重定向到动画模型中去,从而得到逼真的角色动画.实际运行结果表明,从来源广、成本低的视频中完成角色运动提取与合成,从而生成逼真动画的可行性.