Human posture tracking and classification through stereo vision and 3D model matching

The ability of detecting human postures is particularly important in several fields like ambient intelligence, surveillance, elderly care, and human-machine interaction. This problem has been studied in recent years in the computer vision community, but the proposed solutions still suffer from some limitations due to the difficulty of dealing with complex scenes (e.g., occlusions, different view points, etc.). In this article, we present a system for posture tracking and classification based on a stereo vision sensor. The system provides both a robust way to segment and track people in the scene and 3D information about tracked people. The proposed method is based on matching 3D data with a 3D human body model. Relevant points in the model are then tracked over time with temporal filters and a classification method based on hidden Markov models is used to recognize principal postures. Experimental results show the effectiveness of the system in determining human postures with different orientations of the people with respect to the stereo sensor, in presence of partial occlusions and under different environmental conditions

Vision-Based Three Dimensional Hand Interaction In Markerless Augmented Reality Environment

Kemunculan realiti tambahan membolehkan objek maya untuk wujud bersama dengan dunia sebenar dan ini memberi kaedah baru untuk berinteraksi dengan objek maya. Sistem realiti tambahan memerlukan penunjuk tertentu, seperti penanda untuk menentukan bagaimana objek maya wujud dalam dunia sebenar. Penunjuk tertentu mesti diperolehi untuk menggunakan sistem realiti tambahan, tetapi susah untuk seseorang mempunyai penunjuk tersebut pada bila-bila masa. Tangan manusia, yang merupakan sebahagian dari badan manusia dapat menyelesaikan masalah ini. Selain itu, tangan boleh digunakan untuk berinteraksi dengan objek maya dalam dunia realiti tambahan. Tesis ini membentangkan sebuah sistem realiti tambahan yang menggunakan tangan terbuka untuk pendaftaran objek maya dalam persekitaran sebenar dan membolehkan pengguna untuk menggunakan tangan yang satu lagi untuk berinteraksi dengan objek maya yang ditambahkan dalam tiga-matra. Untuk menggunakan tangan untuk pendaftaran dan interaksi dalam realiti tambahan, postur dan isyarat tangan pengguna perlu dikesan. The advent of augmented reality (AR) enables virtual objects to be superimposed on the real world and provides a new way to interact with the virtual objects. AR system requires an indicator to determine for how the virtual objects aligned in the real world. The indicator must first be obtained to access to a particular AR system. It may be inconvenient to have the indicator in reach at all time. Human hand, which is part of the human body may be a solution for this. Besides, hand is also a promising tool for interaction with virtual objects in AR environment. This thesis presents a markerless Augmented Reality system which utilizes outstretched hand for registration of virtual objects in the real environment and enables the users to have three dimensional (3D) interaction with the augmented virtual objects. To employ the hand for registration and interaction in AR, hand postures and gestures that the user perform has to be recognized

Human Motion Analysis Based on Sequential Modeling of Radar Signal and Stereo Image Features

Falls are one of the greatest threats to elderly health in their daily living routines and activities. Therefore, it is very important to detect falls of an elderly in a timely and accurate manner, so that immediate response and proper care can be provided, by sending fall alarms to caregivers. Radar is an effective non-intrusive sensing modality which is well suited for this purpose, which can detect human motions in all types of environments, penetrate walls and fabrics, preserve privacy, and is insensitive to lighting conditions. Micro-Doppler features are utilized in radar signal corresponding to human body motions and gait to detect falls using a narrowband pulse-Doppler radar. Human motions cause time-varying Doppler signatures, which are analyzed using time-frequency representations and matching pursuit decomposition (MPD) for feature extraction and fall detection. The extracted features include MPD features and the principal components of the time-frequency signal representations. To analyze the sequential characteristics of typical falls, the extracted features are used for training and testing hidden Markov models (HMM) in different falling scenarios. Experimental results demonstrate that the proposed algorithm and method achieve fast and accurate fall detections. The risk of falls increases sharply when the elderly or patients try to exit beds. Thus, if a bed exit can be detected at an early stage of this motion, the related injuries can be prevented with a high probability. To detect bed exit for fall prevention, the trajectory of head movements is used for recognize such human motion. A head detector is trained using the histogram of oriented gradient (HOG) features of the head and shoulder areas from recorded bed exit images. A data association algorithm is applied on the head detection results to eliminate head detection false alarms. Then the three dimensional (3D) head trajectories are constructed by matching scale-invariant feature transform (SIFT) keypoints in the detected head areas from both the left and right stereo images. The extracted 3D head trajectories are used for training and testing an HMM based classifier for recognizing bed exit activities. The results of the classifier are presented and discussed in the thesis, which demonstrates the effectiveness of the proposed stereo vision based bed exit detection approach

Possibilities of man-machine interaction through the perception of human gestures

A mesura que les màquines s'utilitzen interaccionant cada cop més amb les persones, la necessitat d'interfícies més amigables esdevé una necessitat creixent. La comunicació oral persona-màquina com una forma d'interacció utilitzant el llenguatge natural és cada vegada més usual. La interpretació dels gestos humans pot, en certes aplicacions, complementar aquesta comunicació oral. Aquest article descriu un sistema d'interpretació dels gestos basat en la visió per computador. El procés d'interpretació realitza la detecció i seguiment d'un operador humà, i a partir dels seus moviments interpreta un conjunt específic d'ordres gestuals, en temps real.As man-machine interaction grows there is an increasing need for friendly interfaces. Human-machine oral communication as a means of natural language interaction is becoming quite common. Interpretation of human gestures can, in some applications, complement such communication. This article describes an interpretation of gestures procedure. The system is based on a computer vision system for the detection and tracking of a human operator and the interpretation of a specific set of human gestures in real time

Automatic visual detection of human behavior: a review from 2000 to 2014

Due to advances in information technology (e.g., digital video cameras, ubiquitous sensors), the automatic detection of human behaviors from video is a very recent research topic. In this paper, we perform a systematic and recent literature review on this topic, from 2000 to 2014, covering a selection of 193 papers that were searched from six major scientific publishers. The selected papers were classified into three main subjects: detection techniques, datasets and applications. The detection techniques were divided into four categories (initialization, tracking, pose estimation and recognition). The list of datasets includes eight examples (e.g., Hollywood action). Finally, several application areas were identified, including human detection, abnormal activity detection, action recognition, player modeling and pedestrian detection. Our analysis provides a road map to guide future research for designing automatic visual human behavior detection systems.This work is funded by the Portuguese Foundation for Science and Technology (FCT - Fundacao para a Ciencia e a Tecnologia) under research Grant SFRH/BD/84939/2012