Browsing by Subject "Bayesian Statistics"

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    Pyroelectric infrared (PIR) sensor based event detection
    (2009) Soyer, Emin Birey
    Pyroelectric Infra-red (PIR) sensors have been extensively used in indoor and outdoor applications as they are low cost, easy to use and widely available. PIR sensors respond to IR radiating objects moving in its viewing range. The current sensors give an output of logical one when they detect a hot object’s motion and a logical zero when there is no moving hot object. In this method, only moving objects can be detected and the rate of false alarm is high. New types of PIR sensors are more sophisticated and more capable. They have a lower false alarm ratio compared to classical ones. Although they can distinguish pets and humans, again they can only be used for detection of hot object motions due to the limitations caused by the usage of the simple comparator structure 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 sensor circuitry. Instead of directly using the output of a classical PIR sensor, an analog signal is extracted from the PIR output and it is sampled. As a result, intelligent signal processing algorithms can be developed using the discrete-time sensor 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 Markov Models (HMM) and Conditional Gaussian Mixture Models (CGMMs). The sampled data is wavelet transformed for feature extraction and then fed into HMMs for analysis. The final decision is reached according to the Markov Model producing the highest probability. Experimental results demonstrate the reliability of the proposed HMM based decision and event classification algorithm.