运动背景下基于粒子滤波的目标跟踪

粒子滤波方法是一种针对非刚性目标运动跟踪的有效工具。运用基于贝叶斯估计的粒子滤波算法,对复杂的运动背景下目标移动进行跟踪。论述了贝叶斯估计理论,推导粒子滤波过程,并将状态粒子决定的区域所对应的色彩直方图用作测量,与目标参考直方图相比较,得出最佳的后验估计。运用窗口粒子平均方法确定目标的坐标,实现跟踪。算法采用单目标以及多目标序列图象进行跟踪实验,并与均值移动(mean-shift)跟踪算法结果进行比较,证明该跟踪算法更为有效。     

Counting moving persons in crowded scenes

The paper presents a method for estimating the number of moving people in a scene for video surveillance applications. The method performance has been characterized on the public database used for the PETS 2009 and 2010 international competitions; the proposed method has been compared, on the same database, with the PETS competitions participants. The system exhibits a high accuracy, and revealed to be so fast that it can be used in real time surveillance applications. The rationale of the method lies on the extraction of suited scale-invariant feature points and the successive selection among them of the moving ones, under the hypothesis that the latter are associated to moving people. The perspective distortions are taken into account by dividing the input frames into smaller horizontal zones, each having (approximately) the same perspective effects. Therefore, the evaluation of the number of people is separately carried out for each zone, and the results are summed up. The most important peculiarity of the proposed method is the availability of a simple training procedure using a brief video sequence that shows a person walking around in the scene; the procedure automatically evaluates all the parameters needed by the system, thus making the method particularly suited for end-user applications.     

Sparse representation for robust abnormality detection in crowded scenes

In crowded scenes, the extracted low-level features, such as optical flow or spatio-temporal interest point, are inevitably noisy and uncertainty. In this paper, we propose a fully unsupervised non-negative sparse coding based approach for abnormality event detection in crowded scenes, which is specifically tailored to cope with feature noisy and uncertainty. The abnormality of query sample is decided by the sparse reconstruction cost from an atomically learned event dictionary, which forms a sparse coding bases. In our algorithm, we formulate the task of dictionary learning as a non-negative matrix factorization (NMF) problem with a sparsity constraint. We take the robust Earth Mover's Distance (EMD), instead of traditional Euclidean distance, as distance metric reconstruction cost function. To reduce the computation complexity of EMD, an approximate EMD, namely wavelet EMD, is introduced and well combined into our approach, without losing performance. In addition, the combination of wavelet EMD with our approach guarantees the convexity of optimization in dictionary learning. To handle both local abnormality detection (LAD) and global abnormality detection, we adopt two different types of spatio-temporal basis. Experiments conducted on four public available datasets demonstrate the promising performance of our work against the state-of-the-art methods.     

Anomaly detection using sparse reconstruction in crowded scenes

Multimedia Tools and Applications - In this paper, we propose an algorithm of anomaly detection in crowded scenes by using sparse representation over the normal bases. First, the histogram of...     

一种时空域联合的机动视频目标精确跟踪方法

提出一种采用Bhattacharyya系数最大化并联合时空域信息的视频目标跟踪方法。时域通过卡尔曼滤波预测目标的运动信息,空域用Camshift算法精确匹配视频目标。由于运动目标机动性比较强,卡尔曼滤波预测的位置和真实位置存在较大的误差,容易导致下一步跟踪失败。采用基于Bhattacharyya系数的由粗到精的核匹配搜索方法,在卡尔曼滤波预测的位置基础上适当扩大搜索范围,通过Bhattacharyya系数最大化确定初始匹配窗口,再用Camshift算法精确匹配视频目标。实验证明该方法对机动快速运动目标具有很高的跟踪精度。     

Scene Aware Detection and Block Assignment Tracking in crowded scenes

How far can human detection and tracking go in real world crowded scenes? Many algorithms often fail in such scenes due to frequent and severe occlusions as well as viewpoint changes. In order to handle these difficulties, we propose Scene Aware Detection (SAD) and Block Assignment Tracking (BAT) that incorporate with some available scene models (e.g. background, layout, ground plane and camera models). The SAD is proposed for accurate detection through utilizing 1) camera model to deal with viewpoint changes by rectifying sub-images, 2) a structural filter approach to handle occlusions based on a feature sharing mechanism in which a three-level hierarchical structure is built for humans, and 3) foregrounds for pruning negative and false positive samples and merging intermediate detection results. Many detection or appearance based tracking systems are prone to errors in occluded scenes because of failures of detectors and interactions of multiple objects. Differently, the BAT formulates tracking as a block assignment process, where blocks with the same label form the appearance of one object. In the BAT, we model objects on two levels, one is the ensemble level to measure how it is like an object by discriminative models, and the other one is the block level to measure how it is like a target object by appearance and motion models. The main advantage of BAT is that it can track an object even when all the part detectors fail as long as the object has assigned blocks. Extensive experiments in many challenging real world scenes demonstrate the efficiency and effectiveness of our approach.     

An information theoretic approach to camera control for crowded scenes

Navigation and monitoring of large and crowded virtual environments is a challenging task and requires intuitive camera control techniques to assist users. In this paper, we present a novel automatic camera control technique providing a scene analysis framework based on information theory. The developed framework contains a probabilistic model of the scene to build entropy and expectancy maps. These maps are utilized to find interest points which represent either characteristic behaviors of the crowd or novel events occurring in the scene. After an interest point is chosen, the camera is updated accordingly to display this point. We tested our model in a crowd simulation environment and it performed successfully. Our method can be integrated into existent camera control modules in computer games, crowd simulations and movie pre-visualization applications.     

Anomaly detection in crowded scenes using motion energy model

Multimedia Tools and Applications - We present a new method for detection of abnormal behaviors in crowded scenes. Based on statistics of low-level feature—optical flow, which describes human...     

面向目标跟踪的混合WMSN设计与实现

设计了一个面向目标跟踪的混合无线多媒体传感器网络,主要实现了低功耗的无线图像传感器节点、无线温度传感器节点和服务器软件。对节点和单Sink下的网络性能进行了测试。结果表明：相比较于Cyclops和MeshEye,无线图像传感器节点的处理速度分别从8 MHz和55 MHz提高到240 MHz;处理器工作在188 MHz时的能耗是52.7 mA,低于Imote在104 MHz时的66 mA工作电流;当错包率接近0时,网络实际带宽约36 kb/s;基于Shape Matching的目标跟踪数据处理策略有效地降低了能量消耗。     

Object and patch based anomaly detection and localization in crowded scenes

Multimedia Tools and Applications - Detecting and localizing anomalies in crowded scenes is an ongoing challenge for public security. Existing approaches are mainly based on patches and...     

A novel dynamic model for multiple pedestrians tracking in extremely crowded scenarios

Tracking hundreds of persons in the large and high density scenarios is a particularly challenging task due to the frequent occlusions and merged measurements. In such circumstances, a stronger dynamic model for prediction usually plays a more important role in the overall tracking process. In this paper, we propose an elaborate dynamic model for multiple pedestrians tracking in the extremely crowded environments. The novelty of this tracking model is that: the global semantic scene structure, local instantaneous crowd flow and the social interactions among persons are taken into account together and combined into an unified approach, which can make the prediction for persons’ motion more powerful and accurate. We apply the proposed model by using an online “tracking-learning” framework, which can not only perform the robust tracking in the extremely crowded scenarios, but also ensures that the entire process is fully automatic and online. The testing is conducted on the JR subway station of Tokyo, and the experimental results show that the system with our tracking model can robustly track more than 180 targets at the same time while the occlusions and merge/split frequently occur.     

Unsupervised deep learning system for local anomaly event detection in crowded scenes

Multimedia Tools and Applications - Anomaly detection in video surveillance is a significant research subject because of its immense use in real-time applications. These days, open spots like...     

Estimation of number of people in crowded scenes using perspectivetransformation

In the past, the estimation of crowd density has become an important topic in the field of automatic surveillance systems. In this paper, the developed system goes one step further to estimate the number of people in crowded scenes in a complex background by using a single image. Therefore, more valuable information than crowd density can be obtained. There are two major steps in this system: recognition of the head-like contour and estimation of crowd size. First, the Haar wavelet transform is used to extract the featured area of the head-like contour, and then the support vector machine is used to classify these featured area as the contour of a head or not. Next, the perspective transforming technique of computer vision is used to estimate crowd size more accurately. Finally, a model world is constructed to test this proposed system and the system is also applied for real-world images     

Abnormal event detection and localization in crowded scenes based on PCANet

Detecting and localizing abnormal events in crowded scenes still remains a challenging task among computer vision community. An unsupervised framework is proposed in this paper to address the problem. Low-level features and optical flows (OF) of video sequences are extracted to represent motion information in the temporal domain. Moreover, abnormal events usually occur in local regions and are closely linked to their surrounding areas in the spatial domain. To extract high-level information from local regions and model the relationship in spatial domain, the first step is to calculate optical flow maps and divide them into a set of non-overlapping sub-maps. Next, corresponding PCANet models are trained using the sub-maps at same spatial location in the optical flow maps. Based on the block-wise histograms extracted by PCANet models, a set of one-class classifiers are trained to predict the anomaly scores of test frames. The framework is completely unsupervised because it utilizes only normal videos. Experiments were carried out on UCSD Ped2 and UMN datasets, and the results show competitive performance of this framework when compared with other state-of-the-art methods.     

A hybrid rendering algorithm for textile objects

Although realistic textile rendering has been widely used in virtual garment and try-on systems, a robust method to simulate textile with a realistic appearance and high fidelity is yet to be established. We propose to use a novel hybrid geometric- and image-based rendering (GIBR) method to achieve photo realistic representation of textile products. The image-based technique, with its radiance synthesis algorithm, enables us to recover the reflectance properties of textile in an environment photo, and thus can render the appearance of textile material. The geometry-based technique, with its traditional illumination model of assigning illumination parameters extracted from the original scene (such as radiance and chroma dispatch), makes it possible to interactively manipulate 3D virtual objects in the “real” environment. Our realistic textile rendering method has advantages over the traditional ones in its easiness to implement and its wide range of applications.