Multiple detections application for indoor tracking using PIR sensor and Kalman filter

Recently, human tracking in multiple indoor environments is getting broadly in demand to enhance security surveillance. Traditional passive video surveillance shown that it has working ineffectively nowadays because the number of cameras has exceeded the ability of operators to monitor them. In this paper, we proposed methods of detecting human presence using Pyroelectric Infrared (PIR) Motion Sensor and tracking people in multiple indoor locations using Kalman filter-based estimation. The proposed method is implemented to analyze the movement of people within the prescribed area and the result will be presented in footprint mapping of the said area. This will further enhanced building security surveillance especially at the sensitive or restricted areas. Experiments for single target tracking in several areas are carried out to verify the application of the developed system. As the results, the maximum error for tracking trajectory reduced from 0.28m to 0.19m and average error for tracking trajectory also reduced from 0.10m to 0.07m after using Kalman filter estimation algorithm

A robust system for counting people using an infrared sensor and a camera

In this paper, a multi-modal solution to the people counting problem in a given area is described. The multi-modal system consists of a differential pyro-electric infrared (PIR) sensor and a camera. Faces in the surveillance area are detected by the camera with the aim of counting people using cascaded AdaBoost classifiers. Due to the imprecise results produced by the camera-only system, an additional differential PIR sensor is integrated to the camera. Two types of human motion: (i) entry to and exit from the surveillance area and (ii) ordinary activities in that area are distinguished by the PIR sensor using a Markovian decision algorithm. The wavelet transform of the continuous-time real-valued signal received from the PIR sensor circuit is used for feature extraction from the sensor signal. Wavelet parameters are then fed to a set of Markov models representing the two motion classes. The affiliation of a test signal is decided as the class of the model yielding higher probability. People counting results produced by the camera are then corrected by utilizing the additional information obtained from the PIR sensor signal analysis. With the proof of concept built, it is shown that the multi-modal system can reduce false alarms of the camera-only system and determines the number of people watching a TV set in a more robust manner. © 2015 Elsevier B.V. All rights reserved

Human Localization and Activity Recognition Using Distributed Motion Sensors

The purpose of this thesis is to localize a human and recognize his/her activities in indoor environments using distributed motion sensors. We propose to use a test bed simulated as mock apartment for conducting our experiments. The two parts of the thesis are localization and activity recognition of the elderly person. We explain complete hardware and software setup used to provide these services. The hardware setup consists of two types of sensor end nodes and two sink nodes. The two types of end nodes are Passive Infrared sensor node and GridEye sensor node. Passive Infrared sensor nodes consist of Passive Infrared sensors for motion detection. GridEye sensor nodes consist of thermal array sensors. Data from these sensors are acquired using Arduino boards and transmitted using Xbee modules to the sink nodes. The sink nodes consist of receiver Xbee modules connected to a computer. The sensor nodes were strategically placed at different place inside the apartment. The thermal array sensor provides 64 pixel temperature values, while the PIR sensor provides binary information about motion in its field of view. Since the thermal array sensor provides more information, they were placed in large rooms such as living room and bed room. While PIR sensors were placed in kitchen and bathroom. Initially GridEye sensors are calibrated to obtain the transformation between pixel and real world coordinates. Data from these sensors were processed on computer and we were able to localize the human inside the apartment. We compared the location accuracy using ground truth data obtained from the OptiTrack system. GridEye sensors were also used for activity recognition. Basic human activities such as sitting, sleeping, standing and walking were recognized. We used Support Vector Machine (SVM) to recognize sitting and sleeping activities. Gait speed of human was used to recognize the standing and walking activities. Experiments were performed to obtain the accuracy of classification for these activities.Electrical Engineerin

Coordinating occupant behavior for building energy and comfort management using multi-agent systems

Issue for Planning Future Cities-Selected papers from the 2010 eCAADe Conference</p