共查询到20条相似文献,搜索用时 31 毫秒
1.
Michela Goffredo Imed Bouchrika John N. Carter Mark S. Nixon 《Multimedia Tools and Applications》2010,50(1):75-94
Many studies have confirmed that gait analysis can be used as a new biometrics. In this research, gait analysis is deployed
for people identification in multi-camera surveillance scenarios. We present a new method for viewpoint independent markerless
gait analysis that does not require camera calibration and works with a wide range of walking directions. These properties
make the proposed method particularly suitable for gait identification in real surveillance scenarios where people and their
behaviour need to be tracked across a set of cameras. Tests on 300 synthetic and real video sequences, with subjects walking
freely along different walking directions, have been performed. Since the choice of the cameras’ characteristics is a key-point
for the development of a smart surveillance system, the performance of the proposed approach is measured with respect to different
video properties: spatial resolution, frame-rate, data compression and image quality. The obtained results show that markerless
gait analysis can be achieved without any knowledge of camera’s position and subject’s pose. The extracted gait parameters
allow recognition of people walking from different views with a mean recognition rate of 92.2% and confirm that gait can be
effectively used for subjects’ identification in a multi-camera surveillance scenario. 相似文献
2.
3.
基于步态的特征识别是一种新兴的生物特征识别技术, 旨在通过人们走路的姿态进行身份和相关属性的分析. 与其他的生物识别技术相比, 基于步态的识别方法具有难隐藏性、非接触性和可远距离使用的优点. 本文设计出一个视频监控场景下基于单视角步态的人体身份及属性识别系统, 该系统通过图像处理方法从复杂的监控视频中实时检测出人体的步态, 经过利用深度学习训练过的算法进行分析后, 获取人体的身份、性别和年龄信息. 实验表明, 系统的身份识别准确率达98.1%, 性别预测准确率达97.1%, 年龄预测平均绝对误差为6.21岁, 实验结果均优于传统基准算法, 且系统开发成本低, 支持实时检测, 能充分满足中小规模步态研究与分析的需要. 相似文献
4.
5.
Zhou Z Prügel-Bennett A Damper RI 《IEEE transactions on pattern analysis and machine intelligence》2006,28(11):1738-1752
Extracting full-body motion of walking people from monocular video sequences in complex, real-world environments is an important and difficult problem, going beyond simple tracking, whose satisfactory solution demands an appropriate balance between use of prior knowledge and learning from data. We propose a consistent Bayesian framework for introducing strong prior knowledge into a system for extracting human gait. In this work, the strong prior is built from a simple articulated model having both time-invariant (static) and time-variant (dynamic) parameters. The model is easily modified to cater to situations such as walkers wearing clothing that obscures the limbs. The statistics of the parameters are learned from high-quality (indoor laboratory) data and the Bayesian framework then allows us to "bootstrap" to accurate gait extraction on the noisy images typical of cluttered, outdoor scenes. To achieve automatic fitting, we use a hidden Markov model to detect the phases of images in a walking cycle. We demonstrate our approach on silhouettes extracted from fronto-parallel ("sideways on") sequences of walkers under both high-quality indoor and noisy outdoor conditions. As well as high-quality data with synthetic noise and occlusions added, we also test walkers with rucksacks, skirts, and trench coats. Results are quantified in terms of chamfer distance and average pixel error between automatically extracted body points and corresponding hand-labeled points. No one part of the system is novel in itself, but the overall framework makes it feasible to extract gait from very much poorer quality image sequences than hitherto. This is confirmed by comparing person identification by gait using our method and a well-established baseline recognition algorithm 相似文献
6.
7.
8.
Analysing human gait has found considerable interest in recent computer vision research. So far, however, contributions to this topic exclusively dealt with the tasks of person identification or activity recognition. In this paper, we consider a different application for gait analysis and examine its use as a means of deducing the physical well-being of people. Understanding the detection of unusual movement patterns as a two-class problem suggests using support vector machines for classification. We present a homeomorphisms between 2D lattices and binary shapes that provides a robust vector space embedding of segmented body silhouettes. Experimental results demonstrate that feature vectors obtained from this scheme are well suited to detect abnormal gait. Wavering, faltering, and falling can be detected reliably across individuals without tracking or recognising limbs or body parts. 相似文献
9.
步态识别作为一种新的生物识别技术,通过人走路的姿势实现对个人身份的识别和认证。步态特征提取是步态识别的关键步骤。采用背景消减法与对称差分法相结合对运动人体分割,采用改进的GVF Snake模型对人体运动步态轮廓进行边缘提取。实验结果表明该方法能准确高效地提取边缘特征作为步态识别的特征。 相似文献
10.
11.
Tracey K. M. Lee Mohammed Belkhatir Saeid Sanei 《Multimedia Tools and Applications》2014,72(3):2833-2869
Global security concerns have raised a proliferation of video surveillance devices. Intelligent surveillance systems seek to discover possible threats automatically and raise alerts. Being able to identify the surveyed object can help determine its threat level. The current generation of devices provide digital video data to be analysed for time varying features to assist in the identification process. Commonly, people queue up to access a facility and approach a video camera in full frontal view. In this environment, a variety of biometrics are available—for example, gait which includes temporal features like stride period. Gait can be measured unobtrusively at a distance. The video data will also include face features, which are short-range biometrics. In this way, one can combine biometrics naturally using one set of data. In this paper we survey current techniques of gait recognition and modelling with the environment in which the research was conducted. We also discuss in detail the issues arising from deriving gait data, such as perspective and occlusion effects, together with the associated computer vision challenges of reliable tracking of human movement. Then, after highlighting these issues and challenges related to gait processing, we proceed to discuss the frameworks combining gait with other biometrics. We then provide motivations for a novel paradigm in biometrics-based human recognition, i.e. the use of the fronto-normal view of gait as a far-range biometrics combined with biometrics operating at a near distance. 相似文献
12.
Analyzing the walking behavior of the public is vital for revealing the need for infrastructure design in a local neighborhood, supporting human-centric urban area development. Traditional walking behavior analysis practices relying on manual on-street surveys to collect pedestrian flow data are labor-intensive and tedious. On the contrary, automated video analytics using surveillance cameras based on computer vision and deep learning techniques appears more effective in generating pedestrian flow statistics. Nevertheless, most existing methods of pedestrian tracking and attribute recognition suffer from several challenging conditions, such as inter-person occlusion and appearance variations, which leads to ambiguous identities and hence inaccurate pedestrian flow statistics.Therefore, this paper proposes a more robust methodology of pedestrian tracking and attribute recognition, facilitating the analysis of pedestrian walking behavior. Specific limitations of a current state-of-the-art method are inferred, based on which several improvement strategies are proposed: 1) incorporating high-level pedestrian attributes to enhance pedestrian tracking, 2) a similarity measure integrating multiple cues for identity matching, and 3) a probation mechanism for more robust identity matching. From our evaluation using two public benchmark datasets, the developed strategies notably enhance the robustness of pedestrian tracking against the challenging conditions mentioned above. Subsequently, the outputs of trajectories and attributes are aggregated into fine-grained pedestrian flow statistics among different pedestrian groups. Overall, our developed framework can support a more comprehensive and reliable decision-making for human-centric planning and design in different urban areas. The framework is also applicable to exploiting pedestrian movement patterns in different scenes for analyses such as urban walkability evaluation. Moreover, the developed mechanisms are generalizable to future researches as a baseline, which provides generic insights of how to fundamentally enhance pedestrian tracking. 相似文献
13.
With the increasing demands of visual surveillance systems, human identification at a distance has gained more attention from the researchers recently. Gait analysis can be used as an unobtrusive biometric measure to identify people at a distance without any attention of the human subjects. We propose a novel effective method for both automatic viewpoint and person identification by using only the silhouette sequence of the gait. The gait silhouettes are nonlinearly transformed into low-dimensional embedding by Gaussian process latent variable model (GP-LVM), and the temporal dynamics of the gait sequences are modeled by hidden Markov models (HMMs). The experimental results show that our method has higher recognition rate than the other methods. 相似文献
14.
Lishani Ait O. Boubchir Larbi Khalifa Emad Bouridane Ahmed 《Multimedia Tools and Applications》2019,78(5):5715-5730
Multimedia Tools and Applications - Human gait recognition is a biometric technique for persons identification based on their walking manner. This paper proposes a novel gait recognition approach... 相似文献
15.
Merlin Linda George Themozhi Govindarajan Kavitha Angamuthu Rajasekaran Sudheer Reddy Bandi 《Computational Intelligence》2020,36(3):1290-1319
Gait recognition has been considered as the emerging biometric technology for identifying the walking behaviors of humans. The major challenges addressed in this article is significant variation caused by covariate factors such as clothing, carrying conditions and view angle variations will undesirably affect the recognition performance of gait. In recent years, deep learning technique has produced a phenomenal performance accuracy on various challenging problems based on classification. Due to an enormous amount of data in the real world, convolutional neural network will approximate complex nonlinear functions in models to develop a generalized deep convolutional neural network (DCNN) architecture for gait recognition. DCNN can handle relatively large multiview datasets with or without using any data augmentation and fine-tuning techniques. This article proposes a color-mapped contour gait image as gait feature for addressing the variations caused by the cofactors and gait recognition across views. We have also compared the various edge detection algorithms for gait template generation and chosen the best from among them. The databases considered for our work includes the most widely used CASIA-B dataset and OULP database. Our experiments show significant improvement in the gait recognition for fixed-view, crossview, and multiview compared with the recent methodologies. 相似文献
16.
17.
Multimodal identification and tracking in smart environments 总被引:1,自引:0,他引:1
We present a model for unconstrained and unobtrusive identification and tracking of people in smart environments and answering
queries about their whereabouts. Our model supports biometric recognition based upon multiple modalities such as face, gait,
and voice in a uniform manner. The key technical idea underlying our approach is to abstract a smart environment by a state transition system in which each state records a set of individuals who are present in various zones of the environment. Since biometric recognition
is inexact, state information is inherently probabilistic in nature. An event abstracts a biometric recognition step, and
the transition function abstracts the reasoning necessary to effect state transitions. In this manner, we are able to integrate
different biometric modalities uniformly and also different criteria for state transitions. Fusion of biometric modalities
is also supported by our model. We define performance metrics for a smart environment in terms of the concepts of ‘precision’
and ‘recall’. We have developed a prototype implementation of our proposed concepts and provide experimental results in this
paper. Our conclusion is that the state transition model is an effective abstraction of a smart environment and serves as
a good basis for developing practical systems. 相似文献
18.
19.
提出了一种基于多特征和多视角信息融合的步态识别方法。应用背景差分和阴影消除获得人体步态轮廓,对人体轮廓使用伪Zernike矩、小波描述子和Procrustes形状分析法进行了特征提取。通过多特征和多视角步态信息融合,完成了基于人体步态特征的身份识别。该方法在CASIA步态数据库上进行了实验,取得了较高的正确识别率,实验结果表明本文所提出的识别方法具有较高的识别性能。 相似文献
20.
Hierarchical database for a multi-camera surveillance system 总被引:1,自引:0,他引:1
This paper presents a framework for event detection and video content analysis for visual surveillance applications. The system is able to coordinate the tracking of objects between multiple camera views, which may be overlapping or non-overlapping. The key novelty of our approach is that we can automatically learn a semantic scene model for a surveillance region, and have defined data models to support the storage of tracking data with different layers of abstraction into a surveillance database. The surveillance database provides a mechanism to generate video content summaries of objects detected by the system across the entire surveillance region in terms of the semantic scene model. In addition, the surveillance database supports spatio-temporal queries, which can be applied for event detection and notification applications. 相似文献