Intelligent Model-Based Tracking of Natural Gait Motion in a Network of Depth Sensors

深度传感器网络中基于智能模型的自然步态运动跟踪

• 批准号: RGPIN-2019-06434
• 负责人: Payandeh, Shahram
• 金额: $ 1.97万
• 依托单位: Simon Fraser University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=681876
• 关键词: Intelligent; Model; Based; Tracking; Natural

Tracking human body by observing its motion patterns in a natural living environment is a fundamental research area with many potential applications. In the past few years, advancement and widespread availability of commodity depth sensors, integrated with cameras have been offering a non-wearable alternative. This research concerns with some aspects of tracking motion of people in a network of sensors located at their natural living environment. Most existing approaches for processing the sensed depth information are incorporating complete or partial 3D depth data. Fundamental to our approach is the intelligent utilization of a collection 2D scan planes of the 3D data as a part of motion and scene representation. As a part of this investigation, it is proposed to investigate methods for determining initial changes in the sensed information due to movements. This detection can be used to assign and schedule sensors in the network and plan paths for mobile sensors. Each of the 1D depth profile in the associated scan planes can be further analyzed using Fourier analysis for defining components of feature vectors for further classification. It is proposed to explore the development of a 2D scan plane planner based on an intelligent particle filter tracking framework. As a part of this estimator, models for the gait movement patterns and postures of the subject are to be incorporated. The model-based estimator compares the sensed feature vectors with stored features associated with a number measured scans and generated key graphical postures and kinematic chain models of various limbs. The kinematic chain model is constructed using hierarchical description of particle filter starting from the kinematics of the torso to more detailed kinematic model of the connected limbs. In this work, we propose to explore the incorporation of visual information of hands and head based on skin color. This additional information will assist in further localization of hand extremities within the depth data for cases when the hand is in contact with the body (torso) or with various other limbs or body parts. We propose to approximate the head orientation during the gait pattern or various activities based on the depth data and face placement. For the cases where body occlusion is inevitable from sensor due to the presence of furniture (such as during the sitting down and raising from a chair), it is proposed to incorporate depth information from 2D scans of the furniture for defining its various feature vectors. We will compare this information with models of the stored features of furniture associated with the living environment to determine both types, position, and orientation of the furniture in relation to the subject.

通过观察人体在自然生活环境中的运动模式来跟踪人体是一个具有许多潜在应用的基础研究领域。在过去的几年里，先进的和广泛可用的商品化深度传感器与相机集成在一起，提供了一种不可穿戴的替代方案。这项研究涉及在人的自然生活环境中的传感器网络中跟踪人的运动的某些方面。大多数现有的处理感测深度信息的方法是结合全部或部分3D深度数据。我们方法的基础是智能地利用3D数据的2D扫描平面集合作为运动和场景表示的一部分。作为这项调查的一部分，建议调查确定由于移动而引起的所感知信息的初始变化的方法。该检测可用于分配和调度网络中的传感器，并规划移动传感器的路径。可以使用傅立叶分析进一步分析相关联的扫描平面中的每一个1D深度轮廓，以定义用于进一步分类的特征向量的分量。提出了一种基于智能粒子滤波跟踪框架的二维扫描平面规划器的开发方案。作为该估计器的一部分，将纳入受试者步态运动模式和姿势的模型。基于模型的估计器将感测到的特征向量与与多个测量扫描相关联的存储特征进行比较，并生成各种肢体的关键图形姿势和运动链模型。运动链模型采用粒子滤波的分层描述方法，从躯干运动学到更详细的四肢运动学模型。在这项工作中，我们提出了基于肤色的手部和头部视觉信息的融合。这些附加信息将有助于在手接触身体(躯干)或其他各种肢体或身体部位的情况下，在深度数据中进一步定位手的四肢。我们提出了在步态模式或各种活动中基于深度数据和人脸放置来近似头部方向。对于由于家具的存在而不可避免地受到传感器遮挡的情况(如坐在椅子上和从椅子上抬起时)，建议结合家具的2D扫描的深度信息来定义其各种特征向量。我们将把这些信息与与生活环境相关的家具的存储特征的模型进行比较，以确定家具相对于主题的类型、位置和方向。