Piroelektrik kızılberisi algılayıcı tabanlı olay tespiti

Pyroelectric Infra-red (PIR) sensors have been extensively used in indoor andoutdoor applications as they are low cost, easy to use and widely available. PIRsensors respond to IR radiating objects moving in its viewing range. The currentsensors give an output of logical one when they detect a hot object's motion anda logical zero when there is no moving hot object. In this method, only movingobjects can be detected and the rate of false alarm is high.New types of PIR sensors are more sophisticated and more capable. Theyhave a lower false alarm ratio compared to classical ones. Although they candistinguish pets and humans, again they can only be used for detection of hotobject motions due to the limitations caused by the usage of the simple comparatorstructure inside. This structure is unalterable, not flexible for development, and not suitable for implementing algorithms.A new approach is developed to use PIR sensors by modifying the sensorcircuitry. Instead of directly using the output of a classical PIR sensor, an analogsignal is extracted from the PIR output and it is sampled. As a result,intelligent signal processing algorithms can be developed using the discrete-timesensor signal. In this way, it is possible to develop human, pet and flame detection methods. It is also possible to find the direction of moving objects and estimate their distances from the sensor. Furthermore, the path of a moving target can be estimated using a PIR sensor array.We focus on object and event classification using sampled PIR sensor signals.Pet, human and flame detection methods are comparatively investigated.Different human motion events are modeled and classifed using Hidden MarkovModels (HMM) and Conditional Gaussian Mixture Models (CGMMs). The sampleddata is wavelet transformed for feature extraction and then fed into HMMsfor analysis. The final decision is reached according to the Markov Model producingthe highest probability. Experimental results demonstrate the reliabilityof the proposed HMM based decision and event classification algorithm.