Depth Images-based Human Detection, Tracking and Activity Recognition Using Spatiotemporal Features and Modified HMM

Human activity recognition using depth information is an emerging and challenging technology in computer vision due to its considerable attention by many practical applications such as smart home/office system, personal health care and 3D video games. This paper presents a novel framework of 3D human body detection, tracking and recognition from depth video sequences using spatiotemporal features and modified HMM. To detect human silhouette, raw depth data is examined to extract human silhouette by considering spatial continuity and constraints of human motion information. While, frame differentiation is used to track human movements. Features extraction mechanism consists of spatial depth shape features and temporal joints features are used to improve classification performance. Both of'these features are fused together to recognize different activities using the modified hidden Markov model (M-HMM). The proposed approach is evaluated on two challenging depth video datasets. Moreover, our system has significant abilities to handle subject's body parts rotation and body parts missing which provide major contributions in human activity recognition.1165Ysciescopuskc

Human Depth Sensors-based Activity Recognition using Spatiotemporal Features and Hidden Markov Model for Smart Environments

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A Depth Video-based Human Detection and Activity Recognition using Multi-features and Embedded Hidden Markov Models for Health Care Monitoring Systems

Increase in number of elderly people who are living independently needs especial care in the form of healthcare monitoring systems. Recent advancements in depth video technologies have made human activity recognition (HAR) realizable for elderly healthcare applications. In this paper, a depth video-based novel method for HAR is presented using robust multi-features and embedded Hidden Markov Models (HMMs) to recognize daily life activities of elderly people living alone in indoor environment such as smart homes. In the proposed HAR framework, initially, depth maps are analyzed by temporal motion identification method to segment human silhouettes from noisy background and compute depth silhouette area for each activity to track human movements in a scene. Several representative features, including invariant, multi-view differentiation and spatiotemporal body joints features were fused together to explore gradient orientation change, intensity differentiation, temporal variation and local motion of specific body parts. Then, these features are processed by the dynamics of their respective class and learned, modeled, trained and recognized with specific embedded HMM having active feature values. Furthermore, we construct a new online human activity dataset by a depth sensor to evaluate the proposed features. Our experiments on three depth datasets demonstrated that the proposed multi-features are efficient and robust over the state of the art features for human action and activity recognition

GIFT: Gesture-Based Interaction by Fingers Tracking, an Interaction Technique for Virtual Environment

Three Dimensional (3D) interaction is the plausible human interaction inside a Virtual Environment (VE). The rise of the Virtual Reality (VR) applications in various domains demands for a feasible 3D interface. Ensuring immersivity in a virtual space, this paper presents an interaction technique where manipulation is performed by the perceptive gestures of the two dominant fingers; thumb and index. The two fingertip-thimbles made of paper are used to trace states and positions of the fingers by an ordinary camera. Based on the positions of the fingers, the basic interaction tasks; selection, scaling, rotation, translation and navigation are performed by intuitive gestures of the fingers. Without keeping a gestural database, the features-free detection of the fingers guarantees speedier interactions. Moreover, the system is user-independent and depends neither on the size nor on the color of the users’ hand. With a case-study project; Interactions by the Gestures of Fingers (IGF) the technique is implemented for evaluation. The IGF application traces gestures of the fingers using the libraries of OpenCV at the back-end. At the front-end, the objects of the VE are rendered accordingly using the Open Graphics Library; OpenGL. The system is assessed in a moderate lighting condition by a group of 15 users. Furthermore, usability of the technique is investigated in games. Outcomes of the evaluations revealed that the approach is suitable for VR applications both in terms of cost and accuracy

Two Hand Gesture Based 3D Navigation in Virtual Environments

Natural interaction is gaining popularity due to its simple, attractive, and realistic nature, which realizes direct Human Computer Interaction (HCI). In this paper, we presented a novel two hand gesture based interaction technique for 3 dimensional (3D) navigation in Virtual Environments (VEs). The system used computer vision techniques for the detection of hand gestures (colored thumbs) from real scene and performed different navigation (forward, backward, up, down, left, and right) tasks in the VE. The proposed technique also allow users to efficiently control speed during navigation. The proposed technique is implemented via a VE for experimental purposes. Forty (40) participants performed the experimental study. Experiments revealed that the proposed technique is feasible, easy to learn and use, having less cognitive load on users. Finally gesture recognition engines were used to assess the accuracy and performance of the proposed gestures. kNN achieved high accuracy rates (95.7%) as compared to SVM (95.3%). kNN also has high performance rates in terms of training time (3.16 secs) and prediction speed (6600 obs/sec) as compared to SVM with 6.40 secs and 2900 obs/sec

Individual Detection-Tracking-Recognition using Depth Activity Images

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