A fast and accurate moving object tracker in active camera model

Detecting and tracking moving objects within a scene is an essential step for high-level machine vision applications such as video content analysis. In this paper, we propose a fast and accurate method for tracking an object of interest in a dynamic environment (active camera model). First, we manually select the region of the object of interest and extract three statistical features, namely the mean, the variance and the range of intensity values of the feature points lying inside the selected region. Then, using the motion information of the background’s feature points and k-means clustering algorithm, we calculate camera motion transformation matrix. Based on this matrix, the previous frame is transformed to the current frame’s coordinate system to compensate the impact of camera motion. Afterwards, we detect the regions of moving objects within the scene using our introduced frame difference algorithm. Subsequently, utilizing DBSCAN clustering algorithm, we cluster the feature points of the extracted regions in order to find the distinct moving objects. Finally, we use the same statistical features (the mean, the variance and the range of intensity values) as a template to identify and track the moving object of interest among the detected moving objects. Our approach is simple and straightforward yet robust, accurate and time efficient. Experimental results on various videos show an acceptable performance of our tracker method compared to complex competitors.     

Fast moving object detection with non-stationary background

The detection of moving objects under a free-moving camera is a difficult problem because the camera and object motions are mixed together and the objects are often detected into the separated components. To tackle this problem, we propose a fast moving object detection method using optical flow clustering and Delaunay triangulation as follows. First, we extract the corner feature points using Harris corner detector and compute optical flow vectors at the extracted corner feature points. Second, we cluster the optical flow vectors using K-means clustering method and reject the outlier feature points using Random Sample Consensus algorithm. Third, we classify each cluster into the camera and object motion using its scatteredness of optical flow vectors. Fourth, we compensate the camera motion using the multi-resolution block-based motion propagation method and detect the objects using the background subtraction between the previous frame and the motion compensated current frame. Finally, we merge the separately detected objects using Delaunay triangulation. The experimental results using Carnegie Mellon University database show that the proposed moving object detection method outperforms the existing other methods in terms of detection accuracy and processing time.     

OPTICS聚类与目标区域概率模型的多运动目标跟踪

目的 针对多运动目标在移动背景情况下跟踪性能下降和准确度不高的问题,本文提出了一种基于OPTICS聚类与目标区域概率模型的方法。方法 首先引入了Harris-Sift特征点检测,完成相邻帧特征点匹配,提高了特征点跟踪精度和鲁棒性;再根据各运动目标与背景运动向量不同这一点,引入了改进后的OPTICS加注算法,在构建的光流图上聚类,从而准确的分离出背景,得到各运动目标的估计区域;对每个运动目标建立一个独立的目标区域概率模型(OPM),随着检测帧数的迭代更新,以得到运动目标的准确区域。结果 多运动目标在移动背景情况下跟踪性能下降和准确度不高的问题通过本文方法得到了很好地解决,Harris-Sift特征点提取、匹配时间仅为Sift特征的17%。在室外复杂环境下,本文方法的平均准确率比传统背景补偿方法高出14%,本文方法能从移动背景中准确分离出运动目标。结论 实验结果表明,该算法能满足实时要求,能够准确分离出运动目标区域和背景区域,且对相机运动、旋转,场景亮度变化等影响因素具有较强的鲁棒性。     

Detecting and tracking of multiple pedestrians using motion, color information and the AdaBoost algorithm

Robust detection and tracking of pedestrians in image sequences are essential for many vision applications. In this paper, we propose a method to detect and track multiple pedestrians using motion, color information and the AdaBoost algorithm. Our approach detects pedestrians in a walking pose from a single camera on a mobile or stationary system. In the case of mobile systems, ego-motion of the camera is compensated for by corresponding feature sets. The region of interest is calculated by the difference image between two consecutive images using the compensated image. Pedestrian detector is learned by boosting a number of weak classifiers which are based on Histogram of Oriented Gradient (HOG) features. Pedestrians are tracked by block matching method using color information. Our tracking system can track pedestrians with possibly partial occlusions and without misses using information stored in advance even after occlusion is ended. The proposed approach has been tested on a number of image sequences, and was shown to detect and track multiple pedestrians very well.     

FOV Constraint Region Analysis and Path Planning for Mobile Robot with Observability to Multiple Feature Points

In visual servoing tasks, it is an important problem to maintain the observability to feature points on objects, which are usually used to calculate the pose between objects and robots. In particular, when the robot’s vision has a limited field of view (FOV) and the points on objects are distributed separately, the problem is more serious. In this paper, based on FOV constraint region analysis and path planning, we propose a novel method for a mobile robot equipped with a pan-tilt camera to keep all points on objects in its view. According to the Horizontal-FOV and Vertical-FOV angular aperture of camera, bounding boxes assisting to calculate the regions with FOV constraint are acquired firstly. Then the region where the robot inside it cannot keep all points in its view can be obtained. Finally the mobile robot plans a shortest path from the current position to the destination, which can avoid the region with FOV constraint. The results of simulations and experiments prove that our method can make mobile robot keep all feature points in its view when it is moving.

     

Heterogeneous multisensor fusion for mapping dynamic environments

In this paper, we propose a heterogeneous multisensor fusion algorithm for mapping in dynamic environments. The algorithm synergistically integrates the information obtained from an uncalibrated camera and sonar sensors to facilitate mapping and tracking. The sonar data is mainly used to build a weighted line-based map via the fuzzy clustering technique. The line weight, with confidence corresponding to the moving object, is determined by both sonar and vision data. The motion tracking is primarily accomplished by vision data using particle filtering and the sonar vectors originated from moving objects are used to modulate the sample weighting. A fuzzy system is implemented to fuse the two sensor data features. Additionally, in order to build a consistent global map and maintain reliable tracking of moving objects, the well-known extended Kalman filter is applied to estimate the states of robot pose and map features. Thus, more robust performance in mapping as well as tracking are achieved. The empirical results carried out on the Pioneer 2DX mobile robot demonstrate that the proposed algorithm outperforms the methods a using homogeneous sensor, in mapping as well as tracking behaviors.     

Tracking and Motion Estimation of the Articulated Object: a Hierarchical Kalman Filter Approach

Real-time motion capture plays a very important role in various applications, such as 3D interface for virtual reality systems, digital puppetry, and real-time character animation. In this paper we challenge the problem of estimating and recognizing the motion of articulated objects using theoptical motion capturetechnique. In addition, we present an effective method to control the articulated human figure in realtime.The heart of this problem is the estimation of 3D motion and posture of an articulated, volumetric object using feature points from a sequence of multiple perspective views. Under some moderate assumptions such as smooth motion and known initial posture, we develop a model-based technique for the recovery of the 3D location and motion of a rigid object using a variation of Kalman filter. The posture of the 3D volumatric model is updated by the 2D image flow of the feature points for all views. Two novel concepts – the hierarchical Kalman filter (KHF) and the adaptive hierarchical structure (AHS) incorporating the kinematic properties of the articulated object – are proposed to extend our formulation for the rigid object to the articulated one. Our formulation also allows us to avoid two classic problems in 3D tracking: the multi-view correspondence problem, and the occlusion problem. By adding more cameras and placing them appropriately, our approach can deal with the motion of the object in a very wide area. Furthermore, multiple objects can be handled by managing multiple AHSs and processing multiple HKFs.We show the validity of our approach using the synthetic data acquired simultaneously from the multiple virtual camera in a virtual environment (VE) and real data derived from a moving light display with walking motion. The results confirm that the model-based algorithm works well on the tracking of multiple rigid objects.     

Moving people tracking with detection by latent semantic analysis for visual surveillance applications

The latent semantic analysis (LSA) has been widely used in the fields of computer vision and pattern recognition. Most of the existing works based on LSA focus on behavior recognition and motion classification. In the applications of visual surveillance, accurate tracking of the moving people in surveillance scenes, is regarded as one of the preliminary requirement for other tasks such as object recognition or segmentation. However, accurate tracking is extremely hard under challenging surveillance scenes where similarity among multiple objects or occlusion among multiple objects occurs. Usual temporal Markov chain based tracking algorithms suffer from the ‘tracking error accumulation problem’. The accumulated errors can finally make the tracking to drift from the target. To handle the problem of tracking drift, some authors have proposed the idea of using detection along with tracking as an effective solution. However, many of the critical issues still remain unsettled in these detection based tracking algorithms. In this paper, we propose a novel moving people tracking with detection based on (probabilistic) LSA. By employing a novel ‘twin-pipeline’ training framework to find the latent semantic topics of ‘moving people’, the proposed detection can effectively detect the interest points on moving people in different indoor and outdoor environments with camera motion. Since the detected interest points on different body parts can be used to locate the position of moving people more accurately, by combining the detection with incremental subspace learning based tracking, the proposed algorithms resolves the problem of tracking drift during each target appearance update process. In addition, due to the time independent processing mechanism of detection, the proposed method is also able to handle the error accumulation problem. The detection can calibrate the tracking errors during updating of each state of the tracking algorithm. Extensive, experiments on various surveillance environments using different benchmark datasets have proved the accuracy and robustness of the proposed tracking algorithm. Further, the experimental comparison results clearly show that the proposed tracking algorithm outperforms the well known tracking algorithms such as ISL, AMS and WSL algorithms. Furthermore, the speed performance of the proposed method is also satisfactory for realistic surveillance applications.     

Feature Detection Performance Based Benchmarking of Motion Deblurring Methods: Applications to Vision for Legged Robots

Dexterous legged robots can move on variable terrain at high speeds. The locomotion of these legged platforms on such terrain causes severe oscillations of the robot body depending on the surface and locomotion speed. Camera sensors mounted on such platforms experience the same disturbances, hence resulting in motion blur. This is a particular corruption of the image and results in information loss further resulting in degradation or loss of important image features. Although motion blur is a significant problem for legged mobile robots, it is of more general interest since it is present in many other handheld/mobile camera applications. Deblurring methods exist in the literature to compensate for blur, however most proposed performance metrics focus on the visual quality of compensated images. From the perspective of computer vision algorithms, feature detection performance is an essential factor that determines vision performance. In this study, we claim that existing image quality based metrics are not suitable to assess the performance of deblurring algorithms when the output is used for computer vision in general and legged robotics in particular. For comparatively evaluating deblurring algorithms, we define a novel performance metric based on the feature detection accuracy on sharp and deblurred images. We rank these algorithms according to the new metric as well as image quality based metrics from the literature and experimentally demonstrate that existing metrics may not be good indicators of algorithm performance, hence good selection criteria for computer vision application. Additionally, noting that a suitable data set to evaluate the effects of motion blur and its compensation for legged platforms is lacking in the literature, we develop a comprehensive multi-sensor data set for that purpose. The data set consists of monocular image sequences collected in synchronization with a low cost MEMS gyroscope, an accurate fiber optic gyroscope and an externally measured ground truth motion data. We make use of this data set for an extensive benchmarking of prominent motion deblurring methods from the literature in terms of existing and the proposed feature based metric.     

Real-time tracking of multiple objects in space-variant vision based on magnocellular visual pathway

In this paper, we propose a space-variant image representation model based on properties of magnocellular visual pathway, which perform motion analysis, in human retina. Then, we present an algorithm for the tracking of multiple objects in the proposed space-variant model. The proposed space-variant model has two effective image representations for object recognition and motion analysis, respectively. Each image representation is based on properties of two types of ganglion cell, which are the beginning of two basic visual pathways; one is parvocellular and the other is magnocellular. Through this model, we can get the efficient data reduction capability with no great loss of important information. And, the proposed multiple objects tracking method is restricted in space-variant image. Typically, an object-tracking algorithm consists of several processes such as detection, prediction, matching, and updating. In particular, the matching process plays an important role in multiple objects tracking. In traditional vision, the matching process is simple when the target objects are rigid. In space-variant vision, however, it is very complicated although the target is rigid, because there may be deformation of an object region in the space-variant coordinate system when the target moves to another position. Therefore, we propose a deformation formula in order to solve the matching problem in space-variant vision. By solving this problem, we can efficiently implement multiple objects tracking in space-variant vision.     

基于特征跟踪和网格路径运动的视频稳像算法

针对手持移动设备拍摄的抖动视频问题,提出了一种基于特征跟踪和网格路径运动的视频稳像算法。通过SIFT算法提取视频帧的特征点,采用KLT算法追踪特征点,利用RANSAC算法估计相邻帧间的仿射变换矩阵,将视频帧划分为均匀的网格,计算视频的运动轨迹,再通过极小化能量函数优化平滑多条网格路径。最后由原相机路径与平滑相机路径的关系,计算相邻帧间的补偿矩阵,利用补偿矩阵对每一帧进行几何变换,从而得到稳定的视频。实验表明,该算法在手持移动设备拍摄的抖动视频中有较好的结果,其中稳像后视频的PSNR平均值相比原抖动视频PSNR值大约提升了11.2 dB。与捆绑相机路径方法相比约提升了2.3 dB。图像间的结构相似性SSIM平均值大约提升了59%,与捆绑相机路径方法相比约提升了3.3%。     

<Emphasis Type="Italic">Video Extruder</Emphasis>: a semi-dense point tracker for extracting beams of trajectories in real time

Two crucial aspects of general-purpose embedded visual point tracking are addressed in this paper. First, the algorithm should reliably track as many points as possible. Second, the computation should achieve real-time video processing, which is challenging on low power embedded platforms. We propose a new multi-scale semi-dense point tracker called Video Extruder, whose purpose is to fill the gap between short-term, dense motion estimation (optical flow) and long-term, sparse salient point tracking. This paper presents a new detector, including a new salience function with low computational complexity and a new selection strategy that allows to obtain a large number of keypoints. Its density and reliability in mobile video scenarios are compared with those of the FAST detector. Then, a multi-scale matching strategy is presented, based on hybrid regional coarse-to-fine and temporal prediction, which provides robustness to large camera and object accelerations. Filtering and merging strategies are then used to eliminate most of the wrong or useless trajectories. Thanks to its high degree of parallelism, the proposed algorithm extracts beams of trajectories from the video very efficiently. We compare it with the state-of-the-art pyramidal Lucas–Kanade point tracker and show that, in short range mobile video scenarios, it yields similar quality results, while being up to one order of magnitude faster. Three different parallel implementations of this tracker are presented, on multi-core CPU, GPU and ARM SoCs. On a commodity 2010 CPU, it can track 8,500 points in a 640 × 480 video at 150 Hz.     

Feature Matching Combining Variable Velocity Model with Reverse Optical Flow

The ORB-SLAM2 based on the constant velocity model is difficult to determine the search window of the reprojection of map points when the objects are in variable velocity motion, which leads to a false matching, with an inaccurate pose estimation or failed tracking. To address the challenge above, a new method of feature point matching is proposed in this paper, which combines the variable velocity model with the reverse optical flow method. First, the constant velocity model is extended to a new variable velocity model, and the expanded variable velocity model is used to provide the initial pixel shifting for the reverse optical flow method. Then the search range of feature points is accurately determined according to the results of the reverse optical flow method, thereby improving the accuracy and reliability of feature matching, with strengthened interframe tracking effects. Finally, we tested on TUM data set based on the RGB-D camera. Experimental results show that this method can reduce the probability of tracking failure and improve localization accuracy on SLAM (Simultaneous Localization and Mapping) systems. Compared with the traditional ORB-SLAM2, the test error of this method on each sequence in the TUM data set is significantly reduced, and the root mean square error is only 63.8% of the original system under the optimal condition.     

视频序列的全景图拼接技术

提出了一种对视频序列进行全景图拼接的方法。主要讨论了有大面积的非刚性运动物体出现的序列，不过此方法也同样适用于无运动物体的纯背景序列。为计算各帧间的投影关系，用仿射模型来描述摄像机运动，并用特征点匹配的方法计算出模型中各参数的值。由于用相关法计算的匹配结果准确率比较低，所以用RANSAC（Random Sampling Consensus）对匹配结果进行了筛选，可以准确求出摄像机运动参数。利用运动参数进行投影，然后用多帧相减并求交集，估计出每帧图像中运动物体存在的区域，最后计算得到了全景图。该方法的结果与前人得到的结果进行了比较，证明用此方法能获得质量较高的全景图。     

融合SPA遮挡分割的多目标跟踪方法

复杂环境下的多目标视频跟踪是计算机视觉领域的一个难点,有效处理目标间遮挡是解决多目标跟踪问题的关键。将运动分割方法引入目标跟踪领域,提出一种融合骨架点指派(SPA)遮挡分割的多目标跟踪方法。由底层光流信息得到骨架点,并估计骨架点遮挡状态;综合使用目标外观、运动、颜色信息等高级语义信息,将骨架点指派给各个目标;最后以骨架点为核,对运动前景密集分类,得到准确的目标前景像素;在粒子滤波器跟踪框架下,使用概率外观模型进行多目标跟踪。在PETS2009数据集上的实验结果表明,文中方法能够改进现有多目标跟踪方法对目标间交互适应性较差的缺点,更好地处理动态遮挡问题。     

Multiple human tracking based on distributed collaborative cameras

Due to the horizon limitation of single camera, it is difficult for single camera based multi-object tracking system to track multiple objects accurately. In addition, the possible object occlusion and ambiguous appearances often degrade the performance of single camera based tracking system. In this paper, we propose a new method of multi-object tracking by using multi-camera network. This method can handle many problems in the existing tracking systems, such as partial and total occlusion, ambiguity among objects, time consuming and etc. Experimental results of the prototype of our system on three pedestrian tracking benchmarks demonstrate the effectiveness and practical utility of the proposed method.     

Multi-feature hierarchical topic models for human behavior recognition

Human behavior recognition is one important task of image processing and surveillance system. One main challenge of human behavior recognition is how to effectively model behaviors on condition of unconstrained videos due to tremendous variations from camera motion,background clutter,object appearance and so on. In this paper,we propose two novel Multi-Feature Hierarchical Latent Dirichlet Allocation models for human behavior recognition by extending the bag-of-word topic models such as the Latent Dirichlet Allocation model and the Multi-Modal Latent Dirichlet Allocation model. The two proposed models with three hierarchies including low-level visual features,feature topics,and behavior topics can effectively fuse two different types of features including motion and static visual features,avoid detecting or tracking the motion objects,and improve the recognition performance even if the features are extracted with a great amount of noise. Finally,we adopt the variational EM algorithm to learn the parameters of these models. Experiments on the YouTube dataset demonstrate the effectiveness of our proposed models.     

Unordered rule discovery using Ant Colony Optimization

In this article,a novel unordered classification rule list discovery algorithm is presented based on Ant Colony Optimization（ACO）. The proposed classifier is compared empirically with two other ACO-based classification techniques on 26 data sets,selected from miscellaneous domains,based on several performance measures. As opposed to its ancestors,our technique has the flexibility of generating a list of IF-THEN rules with unrestricted order. It makes the generated classification model more comprehensible and easily interpretable.The results indicate that the performance of the proposed method is statistically significantly better as compared with previous versions of AntMiner based on predictive accuracy and comprehensibility of the classification model.     

基于双目立体视觉的船舶轨迹跟踪算法研究

双目立体视觉模型通过模拟人眼可以实现对目标距离的测量。为了获得水上船舶实时的运动状态,提出了一种基于双目立体视觉的船舶轨迹跟踪方法。首先,通过摄像机标定、线性空间点三维重建可以测得以相机为中心到船舶的距离,得到船舶的部分运动轨迹;其次,在双目立体视觉测距系统的基础上采用常速(Constant Velocity,CV)模型的方法对船舶运动建模;最后,对建立的船舶运动模型利用强跟踪卡尔曼滤波(Strong Tracking Kalman Filter,STKF)船舶轨迹跟踪的方法跟踪船舶的轨迹并估算目标船舶实时的运动状态。实验结果表明,基于双目立体视觉的船舶轨迹跟踪的方法能有效地跟踪船舶轨迹且适用于工程应用的需求。