Recognizing multiple human activities and tracking full-body pose in unconstrained environments

Visual observations, such as camera images, are hard to obtain for long-term human motion analysis in unconstrained environments. In this paper, we present a method for human full-body pose tracking and activity recognition from measurements of few body-worn inertial orientation sensors. The sensors make our approach insensitive to illumination and occlusions and permit a person to move freely. Since the data provided by inertial sensors is sparse, noisy and often ambiguous, we use a generative prior model of feasible human poses and movements to constrain the tracking problem. Our model consists of several low-dimensional, activity-specific manifold embeddings that significantly restrict the search space for pose tracking. Using a particle filter, our method continuously explores multiple pose hypotheses in the embedding space. An efficient activity switching mechanism governs the distribution of particles across the activity-specific manifold embeddings. Selecting a pose hypothesis that best explains incoming sensor observations simultaneously allows us to classify the activity a person is performing and to estimate the full-body pose. We also derive an effective measure of predictive confidence that enables detecting anomalous movements. Experiments on a multi-person data set containing several activities show that our method can seamlessly detect activity switches and accurately reconstruct full-body poses from the data of only six wearable inertial sensors.     

Agent-based gesture tracking

We describe an agent-based approach to the visual tracking of human hands and head that represents a very useful "middle ground" between the simple model-free tracking and the highly constrained model-based solutions. It combines the simplicity, speed, and flexibility of tracking without using explicit shape models with the ability to utilize domain knowledge and to apply various constraints characteristic of more elaborate model-based tracking approaches. One of the key contributions of our system, called AgenTrac, is that it unifies the power of data-fusion (cue integration) methodologies with a well-organized extended path-coherence-resolution approach designed to handle crossing trajectories of multiple objects. Both approaches are combined in an easily configurable framework. We are not aware of any path-coherence or data-fusion solution in the computer vision literature that equals the breadth, generality, and flexibility of our approach. The AgenTrac system is not limited to tracking only human motion; in fact, one of its main strengths is that it can be easily reconfigured to track many types of objects in video sequences. The multiagent paradigm simplifies the application of basic domain-specific constraints and makes the entire system flexible. The knowledge necessary for effective tracking can be easily encoded in agent hierarchies and agent interactions.     

Efficient iris segmentation method in unconstrained environments

Recently, iris recognition systems have gained increased attention especially in non-cooperative environments. One of the crucial steps in the iris recognition system is the iris segmentation because it significantly affects the accuracy of the feature extraction and iris matching steps. Traditional iris segmentation methods provide excellent results when iris images are captured using near infrared cameras under ideal imaging conditions, but the accuracy of these algorithms significantly decreases when the iris images are taken in visible wavelength under non-ideal imaging conditions. In this paper, a new algorithm is proposed to segments iris images captured in visible wavelength under unconstrained environments. The proposed algorithm reduces the error percentage even in the presence of types of noise include iris obstructions and specular reflection. The proposed algorithm starts with determining the expected region of the iris using the K-means clustering algorithm. The Circular Hough Transform (CHT) is then employed in order to estimate the iris radius and center. A new efficient algorithm is developed to detect and isolate the upper eyelids. Finally, the non-iris regions are removed. Results of applying the proposed algorithm on UBIRIS iris image databases demonstrate that it improves the segmentation accuracy and time.     

基于Agent的复杂数据交换体系的研究

对于复杂数据交换体系而言,一方面难以建立中心节点对数据交换进行统一调度;另一方面各数据节点需要管理大量的数据源、接口软件,协调管理工作繁重.针对复杂数据交换体系的需求特点,提出了一种基于Agent的数据交换框架,并给出了Agent的构成框架、知识库结构及其更新算法、通信消息格式、数据交换流程和相关部署策略.通过Agent的自治性、自身信念和协作机制分散了复杂数据资源调度交换的管理难度,同时也增强了系统的灵活性、鲁棒性.     

Agent-based framework for integrated facility engineering

An agent-based framework for the development of integrated facility engineering environments in support of collaborative design is introduced. This framework aims at integrating design software by allowing better software interoperability. Within their framework, design agents represent various existing design and planning systems that communicate their design information and knowledge partially and incrementally using the Agent Communication Language (ACL). ACL is a formal language proposed as a communication standard for disparate software. It is based on a logic-based language called Knowledge Interchange Format (KIF) and a message protocol called Knowledge Query Manipulation Language (KQML). Design agents are linked and their communication of design information is coordinated via system programs called facilitators in a federation architecture. The federation architecture specifies the way design agents and facilitators communicate in an integrated software environment. In concert with pursuing fundamental research concepts, we have been developing an integrated design software environment that spans different phases of the facility life cycle. This environment serves to demonstrate the primary aspects of this research methodology. In this paper, we first discuss the integration problem and review related research projects. We then present the major aspects of agent-based software engineering methodology and its application to integrated facility engineering. A highlight of the current integrated design environment development is given to illustrate the advantages of this approach. Finally, we summarize and discuss some of the important research issues in light of previous research.     

Agent-based reconfigurable architecture for real-time object tracking

Most conventional object tracking algorithms are implemented on general-purpose processors in software due to its great flexibility. However, the real-time performance is hard to achieve due to the inherent characteristics of the sequential processing of these processors. To tackle this issue, a reconfigurable system-on-chip (rSoC) platform with microprocessors and FPGAs is applied in this paper. To simplify the hardware/software interface, a Belief–Desire–Intention (BDI)-based multi-agent architecture is proposed as the unified framework. Then an agent-based task graph and two heuristic partitioning methods are proposed to partition the hardware and software on an rSoC platform. Compared to the module-based architecture, this BDI-based multi-agent architecture provides more efficiency, flexibility, autonomy, and scalability for the real-time tracking systems. A particle swarm optimization (PSO)-based object detection and tracking algorithm is applied to evaluate the proposed architecture. Extensive experimental results of object tracking demonstrate that the proposed architecture is efficient and highly robust with real-time performance.     

A smartphone-based system for detecting hand tremors in unconstrained environments

The detection of tremors can be crucial for the early diagnosis and proper treatment of some disorders such as Parkinson’s disease. A smartphone-based application has been developed for detecting hand tremors. This application runs in background and distinguishes hand tremors from common daily activities. This application can facilitate the continuous monitoring of patients or the early detection of this symptom. The evaluation analyzes 1770 accelerometer samples with cross-validation for assessing the ability of the system for processing unknown data, obtaining a sensitivity of 95.8 % and a specificity of 99.5 %. It also analyzes continuous data for some volunteers for several days, which corroborated its high performance.     

Multi-target tracking in dense threat environments

A new approach to multi-target tracking is presented for the mid-course stage of the Strategic Defense Initiative (SDI). This approach is based upon a continuum representation of a cluster of flying objects. The velocities of the flying objects are assumed to be embedded into a smooth velocity field. This assumption is based upon the impossibility of encounters in a high-density cluster between the flying objects. Therefore, the problem is reduced to that of identifying a moving continuum based upon consecutive time frame observations. In contradistinction to the previous approaches, here each target is considered as a center of a small continuous neighborhood subjected to a local-affine transformation, and therefore, the target trajectories do not mix. Obviously, their mixture in plane of sensor view is apparent. The approach is illustrated by an example.     

Improving the robustness of gaze tracking under unconstrained illumination conditions

Multimedia Tools and Applications - In human-computer interaction (HCI) applications, the performance degradation of gaze trackers in real-world environments is a critical issue. Typically, gaze...     

Smartphone-based pedestrian tracking in indoor corridor environments

As the use of smartphones spreads rapidly, user localization becomes an important issue for providing diverse location-based services (LBS). While tracking users in outdoor environments is easily done with GPS, the solution for indoor tracking is not trivial. One common technique for indoor user tracking is to employ inertial sensors, but such a system needs to be capable of handling noisy sensors that would normally lead to cumulative locating errors. To reduce such error, additional infrastructure has often been deployed to adjust for these cumulative location errors. As well, previous work has used highly accurate sensors or sensors that are strapped to the body. This paper presents a stand-alone pedestrian tracking system, using only a magnetometer and an accelerometer in a smartphone in indoor corridor environments that are normally laid out in a perpendicular design. Our system provides reasonably accurate pedestrian locations without additional infrastructure or sensors. The experiment results show that the location error is less than approximately 7 m, which is considered adequate for indoor LBS applications.     

Multimodal identification and tracking in smart environments

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.     

Audio-visual active speaker tracking in cluttered indoors environments

We propose a system for detecting the active speaker in cluttered and reverberant environments where more than one person speaks and moves. Rather than using only audio information, the system utilizes audiovisual information from multiple acoustic and video sensors that feed separate audio and video tracking modules. The audio module operates using a particle filter (PF) and an information-theoretic framework to provide accurate acoustic source location under reverberant conditions. The video subsystem combines in 3-D a number of 2-D trackers based on a variation of Stauffer's adaptive background algorithm with spatiotemporal adaptation of the learning parameters and a Kalman tracker in a feedback configuration. Extensive experiments show that gains are to be expected when fusion of the separate modalities is performed to detect the active speaker.     

Aerial tracking of elongated objects in rural environments

In this paper, a new approach to finding and tracking various land cover boundaries such as rivers, agricultural fields, channels and roads for use in visual navigation system of an unmanned aerial vehicle is presented. We use a combination of statistical estimation and optimization techniques for extraction of dominant boundaries in noisy aerial images. A set of perceptual grouping restrictions is used to connect the acquired piecewise boundaries and to find the heading direction of the main boundary. The results are further refined by applying a set of texture and colour cues and eliminating any false hypothesis. To reduce the computation requirements, another approach based on sampled colour values of different land covers is also investigated. Colour characteristics of a set of manually selected windows are compared to select the best attributes needed for discrimination between different land covers in various (natural) lighting conditions. Each frame is then partially scanned and desired environmental features are extracted and classified. The results show that the proposed technique meets the minimum speed and accuracy requirement of aforementioned application and outperforms single-feature object tracking algorithms.

Improved frame-by-frame object pose tracking in complex environments

Pose tracking is an important task in Augmented Reality (AR), interactive systems, and robotic systems. The frame-by-frame pose tracking that is effective in many cases still faces challenges in complex environments such as occlusions, illumination changes and flipping. In this paper, based on the optimization model offered by Ye et al. J Vis Commun Image Represent 44:72–81 (2017), three improvements are further proposed. First, a feature adjustment strategy based on a group of neighbors is offered to alleviate a sharp reduction of features. Then, when the features are no longer well representing the scene of interest, a score model based on a weighted histogram for result evaluations is presented to realize an adaptive interval. Besides, a forward-backward algorithm is provided to improve the accuracy by replacing the detection method with the tracking method. Experimental results manifest the effectiveness of the proposed algorithms.     

Agent-based simulation for horizontal cooperation in logistics and transportation: From the individual to the grand coalition

Horizontal Cooperation is emerging as a way to increase competitiveness in logistics and transportation. Its implementation, however, may be hindered by conflicts and opportunist behavior among the members of the coalition. This paper develops an agent-based simulation model studying the evolution of a coalition over time taking into account various trust-related issues. Different degrees of cooperation, rules for enlarging the coalition with new members as well as a Shapley-based methodology for allocating savings are implemented. To calculate such savings, vehicle routing solution procedures are further integrated. This enables an extensive investigation of the effects of Horizontal Cooperation from both an economic and environmental perspective. Experimental results highlight that significant savings can be achieved with the degree of cooperation and trust-related issues indicating the highest importance.     

ANTS framework for cooperative work environments

Designed to facilitate computer-supported cooperative work, ANTS overcomes the major deficiencies found in earlier groupware toolkits at the conceptual, architectural, and technological levels.     

面向普适环境的协同设计框架研究

提出了一种面向普适环境的协同设计框架。该协同设计框架在现有分布协同对象计算技术和网格服务的基础上,引入了普适环境“随时随地”和“透明”的思想,巧妙地把协同设计技术融入第三代计算环境中。该文讨论了普适计算的相关理论,也涉及了网格计算的相关技术。并且建立了第三代计算环境下分布式协同设计模型。该框架能够很好地利用各种网络资源,随时随地的进行产品设计开发。最后简要介绍该框架在在自主开发的HUST-CAIDS中的初步应用。     

A framework to design 3D interaction assistance in constraints-based virtual environments

The equilibrium of complex systems often depends on a set of constraints. Thus, credible virtual reality modeling of these systems must respect these constraints, in particular for 3D interactions. In this paper, we propose a generic framework for designing assistance to 3D user interaction in constraints-based virtual environment that associates constraints, interaction tasks and assistance tools, such as virtual fixtures (VFs). This framework is applied to design assistance tools for molecular biology analysis. Evaluation shows that VF designed using our framework improve effectiveness of the manipulation task.