定位图像匹配尺度与区域的摄像机位姿实时跟踪

目的 提出一种定位图像匹配尺度及区域的有效算法,通过实现当前屏幕图像特征点与模板图像中对应尺度下部分区域中的特征点匹配,实现摄像机对模板图像的实时跟踪,解决3维跟踪算法中匹配精度与效率问题。方法 在预处理阶段,算法对模板图像建立多尺度表示,各尺度下的图像进行区域划分,在每个区域内采用ORB（oriented FAST and rotated BRIEF）方法提取特征点并生成描述子,由此构建图像特征点的分级分区管理模式。在实时跟踪阶段,对于当前摄像机获得的图像,首先定位该图像所对应的尺度范围,在相应尺度范围内确定与当前图像重叠度大的图像区域,然后将当前图像与模板图像对应的尺度与区域中的特征点集进行匹配,最后根据匹配点对计算摄像机的位姿。结果 利用公开图像数据库（stanford mobile visual search dataset）中不同分辨率的模板图像及更多图像进行实验,结果表明,本文算法性能稳定,配准误差在1个像素左右;系统运行帧率总体稳定在2030 帧/s。结论 与多种经典算法对比,新方法能够更好地定位图像匹配尺度与区域,采用这种局部特征点匹配的方法在配准精度与计算效率方面比现有方法有明显提升,并且当模板图像分辨率较高时性能更好,特别适合移动增强现实应用。     

Detecting bipedal motion from correlated probabilistic trajectories

This paper is about detecting bipedal motion in video sequences by using point trajectories in a framework of classification. Given a number of point trajectories, we find a subset of points which are arising from feet in bipedal motion by analysing their spatio-temporal correlation in a pairwise fashion. To this end, we introduce probabilistic trajectories as our new features which associate each point over a sufficiently long time period in the presence of noise. They are extracted from directed acyclic graphs whose edges represent temporal point correspondences and are weighted with their matching probability in terms of appearance and location. The benefit of the new representation is that it practically tolerates inherent ambiguity for example due to occlusions. We then learn the correlation between the motion of two feet using the probabilistic trajectories in a decision forest classifier. The effectiveness of the algorithm is demonstrated in experiments on image sequences captured with a static camera, and extensions to deal with a moving camera are discussed.     

Human motions representation and recognition by directional motion history images

In this paper, we describe a technique for representing and recognizing human motions using directional motion history images. A motion history image is a single human motion image produced by superposing binarized successive motion image frames so that older frames may have smaller weights. It has, however, difficulty that the latest motion overwrites older motions, resulting in inexact motion representation and therefore incorrect recognition. To overcome this difficulty, we propose directional motion history images which describe a motion with respect to four directions of movement, i.e. up, down, right and left, employing optical flow. The directional motion history images are thus a set of four motion history images defined on four optical flow images. Experimental results show that the proposed technique achieves better performance in the recognition of human motions than the existent motion history images. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Matching actions in presence of camera motion

When the camera viewing an action is moving, the motion observed in the video not only contains the motion of the actor but also the motion of the camera. At each time instant, in addition to the camera motion, a different view of the action is observed. In this paper, we propose a novel method to perform action recognition in presence of camera motion. Proposed method is based on the epipolar geometry between any two views. However, instead of relating two static views using the standard fundamental matrix, we model the motions of independently moving cameras in the equations governing the epipolar geometry and derive a new relation which is referred to as the “temporal fundamental matrix.” Using the temporal fundamental matrix, a matching score between two actions is computed by evaluating the quality of the recovered geometry. We demonstrate the versatility of the proposed approach for action recognition in a number of challenging sequences.     

A framework for heading-guided recognition of human activity

A combined 2D, 3D approach is presented that allows for robust tracking of moving people and recognition of actions. It is assumed that the system observes multiple moving objects via a single, uncalibrated video camera. Low-level features are often insufficient for detection, segmentation, and tracking of non-rigid moving objects. Therefore, an improved mechanism is proposed that integrates low-level (image processing), mid-level (recursive 3D trajectory estimation), and high-level (action recognition) processes. A novel extended Kalman filter formulation is used in estimating the relative 3D motion trajectories up to a scale factor. The recursive estimation process provides a prediction and error measure that is exploited in higher-level stages of action recognition. Conversely, higher-level mechanisms provide feedback that allows the system to reliably segment and maintain the tracking of moving objects before, during, and after occlusion. Heading-guided recognition (HGR) is proposed as an efficient method for adaptive classification of activity. The HGR approach is demonstrated using “motion history images” that are then recognized via a mixture-of-Gaussians classifier. The system is tested in recognizing various dynamic human outdoor activities: running, walking, roller blading, and cycling. In addition, experiments with real and synthetic data sets are used to evaluate stability of the trajectory estimator with respect to noise.     

航摄动态图像序列稳定技术仿真研究

通过分析块匹配算法的优缺点，采用一种其改进的算法应用于动态图像序列的运动矢量检测。该算法先分别把参考图像和当前图像分成若干个运动矢量探测区域，然后在各子区域选取一定数量的代表点进行相关匹配，求出各子区域的局部运动矢量，对所得到的运动矢量进行分析后，决定全局运动矢量。采用这种方法既能保持低的计算量，同时又具有较高的运动矢量检测性能。分析了影响图像的补偿精度主要因素，提出了相应的对策。用该算法对红外航摄图像序列进行稳定仿真试验，证明了该算法用于动态图像序列稳定的有效性。     

Model-based object tracking in monocular image sequences of road traffic scenes

Moving vehicles are detected and tracked automatically in monocular image sequences from road traffic scenes recorded by a stationary camera. In order to exploit the a priori knowledge about shape and motion of vehicles in traffic scenes, a parameterized vehicle model is used for an intraframe matching process and a recursive estimator based on a motion model is used for motion estimation. An interpretation cycle supports the intraframe matching process with a state MAP-update step. Initial model hypotheses are generated using an image segmentation component which clusters coherently moving image features into candidate representations of images of a moving vehicle. The inclusion of an illumination model allows taking shadow edges of the vehicle into account during the matching process. Only such an elaborate combination of various techniques has enabled us to track vehicles under complex illumination conditions and over long (over 400 frames) monocular image sequences. Results on various real-world road traffic scenes are presented and open problems as well as future work are outlined.     

A methodology for rapid illumination-invariant face recognition using image processing filters

Achieving illumination invariance in the presence of large pose changes remains one of the most challenging aspects of automatic face recognition from low resolution imagery. In this paper, we propose a novel recognition methodology for their robust and efficient matching. The framework is based on outputs of simple image processing filters that compete with unprocessed greyscale input to yield a single matching score between two individuals. Specifically, we show how the discrepancy of the illumination conditions between query input and training (gallery) data set can be estimated implicitly and used to weight the contributions of the two competing representations. The weighting parameters are representation-specific (i.e. filter-specific), but not gallery-specific. Thus, the computationally demanding, learning stage of our algorithm is offline-based and needs to be performed only once, making the added online overhead minimal. Finally, we describe an extensive empirical evaluation of the proposed method in both a video and still image-based setup performed on five databases, totalling 333 individuals, over 1660 video sequences and 650 still images, containing extreme variation in illumination, pose and head motion. On this challenging data set our algorithm consistently demonstrated a dramatic performance improvement over traditional filtering approaches. We demonstrate a reduction of 50–75% in recognition error rates, the best performing method-filter combination correctly recognizing 97% of the individuals.     

A survey on vision-based human action recognition

Vision-based human action recognition is the process of labeling image sequences with action labels. Robust solutions to this problem have applications in domains such as visual surveillance, video retrieval and human–computer interaction. The task is challenging due to variations in motion performance, recording settings and inter-personal differences. In this survey, we explicitly address these challenges. We provide a detailed overview of current advances in the field. Image representations and the subsequent classification process are discussed separately to focus on the novelties of recent research. Moreover, we discuss limitations of the state of the art and outline promising directions of research.     

基于全局运动补偿的多运动目标检测方法研究

提出了一种动态背景下的对多个运动目标检测的完整方法.利用基于宏块匹配的六参数全局运动估计方法进行全局运动补偿,有效地消除了摄像机在非稳定运动情况下对目标检测性能带来的不利影响.同时在宏块匹配前进行了预处理,通过预判提取纹理信息丰富的宏块,并在宏块匹配的过程中采用九点十字搜索算法取代传统的三步搜索算法,减少了匹配数据量....     

基于压缩域行为描述子的视频检索新技术

构造了一个基于压缩域行为描述子的检索模型CMRS.在CMRS中,针对特征提取部分提出了一种基于宏块连接跟踪机制的运动轨迹提取算法和一种参数行为估计算法,并且对参数化运动原始的匹配准则（PSSM）加以改进,构造了新的基于速度的相似匹配准则.最后在检索模型的实现阶段,实现了模型的总体框架.