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.

通过在自然生活环境中观察其运动模式来跟踪人体是一个基本的研究领域，具有许多潜在的应用。在过去的几年中，与相机集成的商品深度传感器的进步和广泛的可用性一直在提供不可磨损的替代方案。这项研究涉及在自然生活环境的传感器网络中跟踪人们的运动的某些方面。 处理感知深度信息的大多数现有方法都包含了完整或部分3D深度数据。我们方法的基础是3D数据的集合2D扫描平面的智能利用，作为运动和场景表示的一部分。作为这项研究的一部分，建议研究通过运动引起的感知信息的初始变化的方法。该检测可用于在网络中分配和安排传感器，并计划移动传感器的路径。可以使用傅立叶分析来进一步分析相关扫描平面中的1D深度曲线，以定义特征向量的组件以进行进一步分类。建议根据智能粒子过滤器跟踪框架探索2D扫描平面计划器的开发。作为该估计量的一部分，将纳入步态运动模式和姿势的模型。基于模型的估计器将感知的特征向量与与数量测量的扫描和生成的关键图形姿势和各种四肢的运动链模型相关的存储特征进行了比较。运动链模型是使用从躯干的运动学开始到连接肢体的更详细的运动学模型的粒子滤波器的层次描述构建的。在这项工作中，我们建议根据肤色探索手和头部的视觉信息。这些附加信息将有助于进一步将手在与身体接触（躯干）或其他各种四肢或身体部位接触的情况下的深度数据中进一步定位。 我们建议根据步态模式或基于深度数据和面部放置的各种活动近似头部方向。对于由于家具的存在（例如坐下并从椅子上抬高时）而不可避免地会从传感器上遮挡，因此建议将家具2D扫描中的深度信息结合起来，以定义其各种特征向量。我们将将这些信息与与生活环境相关的家具的存储特征进行比较，以确定家具相对于受试者的类型，位置和方向。