Crowd behaviors analysis and abnormal detection based on surveillance data

Crowd analysis and abnormal trajectories detection are hot topics in computer vision and pattern recognition. As more and more video monitoring equipments are installed in public places for public security and management, researches become urgent to learn the crowd behavior patterns through the trajectories obtained by the intelligent video surveillance technology. In this paper, the FCM (Fuzzy c-means) algorithm is adopted to cluster the source points and sink points of trajectories that are deemed as critical points into several groups, and then the trajectory clusters can be acquired. The feature information statistical histogram for each trajectory cluster which contains the motion information will be built after refining them with Hausdorff distances. Eventually, the local motion coherence between test trajectories and refined trajectory clusters will be used to judge whether they are abnormal.     

View-Invariant Representation and Recognition of Actions

Analysis of human perception of motion shows that information for representing the motion is obtained from the dramatic changes in the speed and direction of the trajectory. In this paper, we present a computational representation of human action to capture these dramatic changes using spatio-temporal curvature of 2-D trajectory. This representation is compact, view-invariant, and is capable of explaining an action in terms of meaningful action units called dynamic instants and intervals. A dynamic instant is an instantaneous entity that occurs for only one frame, and represents an important change in the motion characteristics. An interval represents the time period between two dynamic instants during which the motion characteristics do not change. Starting without a model, we use this representation for recognition and incremental learning of human actions. The proposed method can discover instances of the same action performed by differentpeople from different view points. Experiments on 47 actions performed by 7 individuals in an environment with no constraints shows the robustness of the proposed method.     

View-invariant motion trajectory-based activity classification and recognition

Motion trajectories provide rich spatio-temporal information about an object's activity. The trajectory information can be obtained using a tracking algorithm on data streams available from a range of devices including motion sensors, video cameras, haptic devices, etc. Developing view-invariant activity recognition algorithms based on this high dimensional cue is an extremely challenging task. This paper presents efficient activity recognition algorithms using novel view-invariant representation of trajectories. Towards this end, we derive two Affine-invariant representations for motion trajectories based on curvature scale space (CSS) and centroid distance function (CDF). The properties of these schemes facilitate the design of efficient recognition algorithms based on hidden Markov models (HMMs). In the CSS-based representation, maxima of curvature zero crossings at increasing levels of smoothness are extracted to mark the location and extent of concavities in the curvature. The sequences of these CSS maxima are then modeled by continuous density (HMMs). For the case of CDF, we first segment the trajectory into subtrajectories using CDF-based representation. These subtrajectories are then represented by their Principal Component Analysis (PCA) coefficients. The sequences of these PCA coefficients from subtrajectories are then modeled by continuous density hidden Markov models (HMMs). Different classes of object motions are modeled by one Continuous HMM per class where state PDFs are represented by GMMs. Experiments using a database of around 1750 complex trajectories (obtained from UCI-KDD data archives) subdivided into five different classes are reported.     

Activity classification and anomaly detection using m-mediods based modelling of motion patterns

Techniques for video object motion analysis, behaviour recognition and event detection are becoming increasingly important with the rapid increase in demand for and deployment of video surveillance systems. Motion trajectories provide rich spatiotemporal information about an object's activity. This paper presents a novel technique for classification of motion activity and anomaly detection using object motion trajectory. In the proposed motion learning system, trajectories are treated as time series and modelled using modified DFT-based coefficient feature space representation. A modelling technique, referred to as m-mediods, is proposed that models the class containing n members with m mediods. Once the m-mediods based model for all the classes have been learnt, the classification of new trajectories and anomaly detection can be performed by checking the closeness of said trajectory to the models of known classes. A mechanism based on agglomerative approach is proposed for anomaly detection. Four anomaly detection algorithms using m-mediods based representation of classes are proposed. These includes: (i)global merged anomaly detection (GMAD), (ii) localized merged anomaly detection (LMAD), (iii) global un-merged anomaly detection (GUAD), and (iv) localized un-merged anomaly detection (LUAD). Our proposed techniques are validated using variety of simulated and complex real life trajectory datasets.     

室外未知环境下的AGV地貌主动探索感知

智能机器人对复杂地貌环境的识别一直是机器人应用领域研究的前沿问题,移动机器人在不同的地貌上采取的运动方式并非一成不变,所以选择的运动方式对于迅速准确识别所处地貌的类型至关重要。针对该问题本文提出了一种基于贝叶斯框架的主动感知探索方法,使移动机器人能够主动探索有兴趣的运动方式并且感知识别和运动之间的匹配关系,可以优化在地貌识别之中的模糊不确定性;为了进一步验证实验的可靠性,还使用了被动感知策略来比较和分析不同策略之间的差异。实验结果表明:主动感知方法能够规划出有效的地貌识别动作序列,能够引导移动机器人主动感知目标地貌,该框架对于室外未知环境下主动感知后的地貌识别效果优于被动感知。     

GloveSignature: A Virtual-Reality-Based System for Dynamic Signature Verification

A survey of the principal schemes in the literature suggested that a new way of addressing the problem of signature recognition be formulated in order to find a satisfactory solution for eliminating random forgeries. A fundamental problem in the field of off-line signature recognition is the lack of a pertinent shape representation or shape factor. This paper introduces a novel idea for a dynamic signature recognition system. An initial attempt is presented to demonstrate the data glove as an effective high-bandwidth data entry device for signature recognition. GloveSignature is a virtual-reality-based environment to support the signing process. The proposed approach retains the power to discriminate against forgeries. This paper extends the use of instrumented data gloves—gloves equipped with sensors for detecting finger bend and hand position and orientation for recognizing hand signatures. Several researchers have already explored the use of gloves in other application areas but using the gloves for the recognition of hand signatures has never been reported. An attempt is made in this research to explore the feasibility of using the 5th Glove in on-line signature recognition. Two hundred signatures were collected from 20 subjects, and features were extracted. We demonstrate the effectiveness of a hybrid technique, which is based on both the most discriminating eigenfeatures and the self-organizing maps (SOFMs) for signature recognition.     

基于双线性对的可证明安全的环签名和代理环签名

2001年的亚密会上,Revest,Shamir和Tauman正式提出了环签名[1]的概念。环签名可以被看做是简化了的群签名,它保护签名者的匿名性不被泄露。文章提出了一种新的环签名方案和代理环签名方案,都是基于双线性对的,并分析了新方案的安全性。     

Behavior pattern extraction by trajectory analysis

Trajectory clustering and behavior pattern extraction are the foundations of research into activity perception of objects in motion. In this paper, a new framework is proposed to extract behavior patterns through trajectory analysis. Firstly, we introduce directional trimmed mean distance (DTMD), a novel method used to measure similarity between trajectories. DTMD has the attributes of anti-noise, self-adaptation and the capability to determine the direction for each trajectory. Secondly, we use a hierarchical clustering algorithm to cluster trajectories. We design a length-weighted linkage rule to enhance the accuracy of trajectory clustering and reduce problems associated with incomplete trajectories. Thirdly, the motion model parameters are estimated for each trajectory’s classification, and behavior patterns for trajectories are extracted. Finally, the difference between normal and abnormal behaviors can be distinguished.     

Motion-based behaviour learning, profiling and classification in the presence of anomalies

Techniques for understanding video object motion activity are becoming increasingly important with the widespread adoption of CCTV surveillance systems. Motion trajectories provide rich spatiotemporal information about an object's activity. This paper presents a novel technique for clustering and classification of motion. In the proposed motion learning system, trajectories are treated as time series and modelled using modified DFT (discrete fourier transform)-based coefficient feature space representation. A framework (iterative HSACT-LVQ (hierarchical semi-agglomerative clustering-learning vector quantization)) is proposed for learning of patterns in the presence of significant number of anomalies in training data. A novel modelling technique, referred to as m-Mediods, is also proposed that models the class containing n members with m Mediods. Once the m-Mediods-based model for all the classes have been learnt, the classification of new trajectories and anomaly detection can be performed by checking the closeness of said trajectory to the models of known classes. A mechanism based on agglomerative approach is proposed for anomaly detection. Our proposed techniques are validated using variety of simulated and complex real life trajectory data sets.     

Trajectory planning and control of a Stewart platform-based end-effector with passive compliance for part assembly

This paper presents the kinematic analysis and trajectory planning for a six-degrees-of-freedom end-effector whose design is based on the Stewart platform mechanism. The end-effector is composed of two platforms and six linear actuators driven by stepper motors. A spring-loaded platform is used to provide passive compliance to the end-effector during a part assembly. A closed-form solution is derived for the inverse kinematic transformation and a computationally effective numerical solution is obtained for the forward kinematic transformation using the Newton-Raphson method. Three trajectory planning schemes, two for fine motion and one for gross motion are developed. Experimental results of tracking various test paths are presented.     

Rule-based trajectory segmentation for modeling hand motion trajectory

In this paper, we propose a simple but effective method of modeling hand gestures based on the angles and angular change rates of the hand trajectories. Each hand motion trajectory is composed of a unique series of straight and curved segments. In our Hidden Markov Model (HMM) implementation, these trajectories are modeled as a connected series of states analogous to the series of phonemes in speech recognition. The novelty of the work presented herein is that it provides an automated process of segmenting gesture trajectories based on a simple set of threshold values in the angular change measure. In order to represent the angular distribution of each separated state, the von Mises distribution is used. A likelihood based state segmentation was implemented in addition to the threshold based method to ensure that the gesture sets are segmented consistently. The proposed method can separate each angular state of the training data at the initialization step, thus providing a solution to mitigate the ambiguities on initializing the HMM. The effectiveness of the proposed method was demonstrated by the higher recognition rates in the experiments compared to the conventional methods.     

Compression and recognition of dance gestures using a deformable model

In this paper, we aim for the recognition of a set of dance gestures from contemporary ballet. Our input data are motion trajectories followed by the joints of a dancing body provided by a motion-capture system. It is obvious that direct use of the original signals is unreliable and expensive. Therefore, we propose a suitable tool for non-uniform sub-sampling of spatiotemporal signals. The key to our approach is the use of a deformable model to provide a compact and efficient representation of motion trajectories. Our dance gesture recognition method involves a set of hidden Markov models (HMMs), each of them being related to a motion trajectory followed by the joints. The recognition of such movements is then achieved by matching the resulting gesture models with the input data via HMMs. We have validated our recognition system on 12 fundamental movements from contemporary ballet performed by four dancers. This revised version was published online in November 2004 with corrections to the section numbers. Ballet Atlantique Régine Chopinot.     

基于Boosting RBF神经网络的人体行为识别

提出一种基于Boosting RBF神经网络的人体行为识别方法,该方法利用规范化的运动历史图像（MHI）进行图像序列表示,从中提取Zernike矩的统计描述特征,然后提出Adaboost算法自适应地选择图像序列的特征作为RBF神经网络的输入,为了进一步提高神经网络的泛化能力,采用一种调整权值分布,限制权重扩张的改进的Boosting方法,分类器以加权投票方式进行分类决策。实验结果表明,提出的方法能够有效地识别人体运动类别。     

基于时空约束密度聚类的停留点识别方法

轨迹停留点的识别是轨迹分析、出行活动语义挖掘的关键。针对基于密度聚类的停留点识别方法对时空信息的表达缺陷,提出新的时空约束停留点识别方法,在密度聚类中引入轨迹的间接时空特征表示,将具有时空相似性的轨迹点进行聚合;采用与聚类过程相统一的时空特征约束对轨迹簇进行细粒度识别。算法在进行约束的时候再次利用到聚类时候所用的输入数据特征,特征的充分利用提高了识别的准确率。实验结果验证了本文方法的有效性。     

一种新的证实数字签名方案

签名者的安全性和证实签名的“不可见性”是一个证实数字签名方案必须具备的两个重要特性。针对现存证实数字签名方案或者低效或者不安全的缺点,论文通过对现有的证实数字签名方案进行研究,构造了一种新的交互式零知识证实与否认协议,进而提出一种新的证实数字签名方案。分析表明,该方案是一种安全而高效的证实数字签名方案。