Browsing by Subject "Detection algorithm"

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    Multiperson tracking with a network of ultrawideband radar sensors based on gaussian mixture PHD filters
    (Institute of Electrical and Electronics Engineers Inc., 2015) Gulmezoglu, B.; Guldogan, M. B.; Gezici, Sinan
    In this paper, we investigate the use of Gaussian mixture probability hypothesis density filters for multiple person tracking using ultrawideband (UWB) radar sensors in an indoor environment. An experimental setup consisting of a network of UWB radar sensors and a computer is designed, and a new detection algorithm is proposed. The results of this experimental proof-of-concept study show that it is possible to accurately track multiple targets using a UWB radar sensor network in indoor environments based on the proposed approach. © 2014 IEEE.
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    Vibrasyon ve PIR algılayıcılar kullanılarak çevre destekli akıllı ev tasarımı
    (IEEE, 2013-04) Yazar, Ahmet; Çetin, A. Enis
    Intelligent ambient assisted living systems for elderly and handicapped people become affordable with the recent advances in computer and sensor technologies. In this paper, fall detection algorithm using multiple passive infrared sensors is developed. As a novel method for detecting a falling person, two passive infrared sensors are used concurrently in a room and developed a determination algorithm depending on the height at which the falling event is happened. Motionles detection system is integrated with the falling person detection system to provide a complete solution. Detection algorithms are implemented using embedded microprocessors. © 2013 IEEE.
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    Wildfire detection using LMS based active learning
    (IEEE, 2009-04) Töreyin, B. Uğur; Çetin, A. Enis
    A computer vision based algorithm for wildfire detection is developed. The main detection algorithm is composed of four sub-algorithms detecting (i) slow moving objects, (ii) gray regions, (iii) rising regions, and (iv) shadows. Each algorithm yields its own decision as a real number in the range [-1,1] at every image frame of a video sequence. Decisions from subalgorithms are fused using an adaptive algorithm. In contrast to standard Weighted Majority Algorithm (WMA), weights are updated using the Least Mean Square (LMS) method in the training (learning) stage. The error function is defined as the difference between the overall decision of the main algorithm and the decision of an oracle, who is the security guard of the forest look-out tower. ©2009 IEEE.