Volatile organic compound plume detection using wavelet analysis of video

A video based method to detect volatile organic compounds (VOC) leaking out of process equipments used in petrochemical refineries is developed. Leaking VOC plume from a damaged component causes edges present in image frames loose their sharpness. This leads to a decrease in the high frequency content of the image. The background of the scene is estimated and decrease of high frequency energy of the scene is monitored using the spatial wavelet transforms of the current and the background images. Plume regions in image frames are analyzed in low-band sub-images, as well. Image frames are compared with their corresponding low-band images. A maximum likelihood estimator (MLE) for adaptive threshold estimation is also developed in this paper. © 2008 IEEE

Online detection of fire in video

This paper describes an online learning based method to detect flames in video by processing the data generated by an ordinary camera monitoring a scene. Our fire detection method consists of weak classifiers based on temporal and spatial modeling of flames. Markov models representing the flame and flame colored ordinary moving objects are used to distinguish temporal flame flicker process from motion of flame colored moving objects. Boundary of flames are represented in wavelet domain and high frequency nature of the boundaries of fire regions is also used as a clue to model the flame flicker spatially. Results from temporal and spatial weak classifiers based on flame flicker and irregularity of the flame region boundaries are updated online to reach a final decision. False alarms due to ordinary and periodic motion of flame colored moving objects are greatly reduced when compared to the existing video based fire detection systems. © 2007 IEEE

Fall detection using single-tree complex wavelet transform

The goal of Ambient Assisted Living (AAL) research is to improve the quality of life of the elderly and handicapped people and help them maintain an independent lifestyle with the use of sensors, signal processing and telecommunications infrastructure. Unusual human activity detection such as fall detection has important applications. In this paper, a fall detection algorithm for a low cost AAL system using vibration and passive infrared (PIR) sensors is proposed. The single-tree complex wavelet transform (ST-CWT) is used for feature extraction from vibration sensor signal. The proposed feature extraction scheme is compared to discrete Fourier transform and mel-frequency cepstrum coefficients based feature extraction methods. Vibration signal features are classified into "fall" and "ordinary activity" classes using Euclidean distance, Mahalanobis distance, and support vector machine (SVM) classifiers, and they are compared to each other. The PIR sensor is used for the detection of a moving person in a region of interest. The proposed system works in real-time on a standard personal computer. © 2012 Elsevier B.V. All rights reserved

Computer vision based method for real-time fire and flame detection

This paper proposes a novel method to detect fire and/or flames in real-time by processing the video data generated by an ordinary camera monitoring a scene. In addition to ordinary motion and color clues, flame and fire flicker is detected by analyzing the video in the wavelet domain. Quasi-periodic behavior in flame boundaries is detected by performing temporal wavelet transform. Color variations in flame regions are detected by computing the spatial wavelet transform of moving fire-colored regions. Another clue used in the fire detection algorithm is the irregularity of the boundary of the fire-colored region. All of the above clues are combined to reach a final decision. Experimental results show that the proposed method is very successful in detecting fire and/or flames. In addition, it drastically reduces the false alarms issued to ordinary fire-colored moving objects as compared to the methods using only motion and color clues. © 2005 Elsevier B.V. All rights reserved

Fire detection in video using LMS based active learning

In this paper, a video based algorithm for fire and flame detection is developed. In addition to ordinary motion and color clues, flame flicker is distinguished from motion of flame colored moving objects using Markov models. Irregular nature of flame boundaries is detected by performing temporal wavelet analysis using Hidden Markov Models as well. Color variations in fire is detected by computing the spatial wavelet transform of moving fire-colored regions. Boundary of flames are represented in wavelet domain and irregular nature of the boundaries of fire regions is also used as an indication of the flame flicker. Decisions from sub-algorithms are linearly combined using an adaptive active fusion method. The main detection algorithm is composed of four sub-algorithms (i) detection of fire colored moving objects, (ii) temporal, and (iii) spatial wavelet analysis for flicker detection and (iv) contour analysis of fire colored region boundaries. Each algorithm yields a continuous decision value as a real number in the range [-1, 1] at every image frame of a video sequence. Decision values from sub-algorithms are fused using an adaptive algorithm in which weights are updated using the least mean square (LMS) method in the training (learning) stage. © 2009 Springer Science+Business Media, LLC

An experimental setup for performance analysis of an online adaptive cooperative spectrum sensing scheme for both in-phase and quadrature branches

Spectrum sensing is one of the most essential characteristics of cognitive radios (CRs). Robustness and adaptation to varying wireless propagation scenarios without compromising the sensing accuracy are desirable features of any spectrum sensing method to be deployed in CR systems. In this study, an online adaptive cooperation technique for spectrum sensing is proposed in order to maintain the level of reliability and performance. Cooperation is achieved by sensors which employ energy detection. These sensors send their output to a center where data fusion operation is carried out in an online and adaptive manner. Adaptation is realized by the use of orthogonal projections onto convex sets (POCS). In conjunction with the proposed method, an end-to-end methodology for a flexible experimental setup is also proposed in this study. This setup is arranged to emulate the proposed adaptive cooperation scheme for spectrum sensing and validate its practical use in cognitive radio systems. Comparative performance results for both inphase and quadrature branches are presented. © 2011 IEEE

An online adaptive cooperation scheme for spectrum sensing based on a second-order statistical method

Spectrum sensing is one of the most important features of cognitive radio (CR) systems. Although spectrum sensing can be performed by a single CR, it is shown in the literature that cooperative techniques, including multiple CRs/sensors, improve the performance and reliability of spectrum sensing. Existing cooperation techniques usually assume a static communication scenario between the unknown source and sensors along with a fixed propagation environment class. In this paper, an online adaptive cooperation scheme is proposed for spectrum sensing to maintain the level of sensing reliability and performance under changing channel and environmental conditions. Each cooperating sensor analyzes second-order statistics of the received signal, which undergoes both correlated fast and slow fading. Autocorrelation estimation data from sensors are fused together by an adaptive weighted linear combination at the fusion center. Weight update operation is performed online through the use of orthogonal projection onto convex sets. Numerical results show that the performance of the proposed scheme is maintained for dynamically changing characteristics of the channel between an unknown source and sensors, even under different physical propagation environments. In addition, it is shown that the proposed cooperative scheme, which is based on second-order detectors, yields better results compared with the same fusion mechanism that is based on conventional energy detectors. © 2012 IEEE

Fire detection in infrared video using wavelet analysis

A novel method to detect flames in infrared (IR) video is proposed. Image regions containing flames appear as bright regions in IR video. In addition to ordinary motion and brightness clues, the flame flicker process is also detected by using a hidden Markov model (HMM) describing the temporal behavior. IR image frames are also analyzed spatially. Boundaries of flames are represented in wavelet domain and the high frequency nature of the boundaries of fire regions is also used as a clue to model the flame flicker. All of the temporal and spatial clues extracted from the IR video are combined to reach a final decision. False alarms due to ordinary bright moving objects are greatly reduced because of the HMM-based flicker modeling and wavelet domain boundary modeling. © 2007 Society of Photo-Optical Instrumentation Engineers

An experimental validation of an online adaptive cooperation scheme for spectrum sensing

Cooperative spectrum sensing methods in the literature assume a static communication scenario with fixed channel and propagation environment characteristics. In order to maintain the level of sensing reliability and performance under changing channel and environment conditions, in this study, an online adaptive cooperation scheme is proposed. Energy detection data from each cooperating sensor are fused together by an adaptive weighted linear combination at the fusion center. Weight update operation is performed online through the use of orthogonal projections onto convex sets (POCS). Also, in this paper, an end-to-end methodology for a flexible experimental setup is proposed. This setup is specifically deployed to emulate the proposed adaptive cooperation scheme for spectrum sensing and validate its practical use in cognitive radio systems. © 2011 IEEE

Fire detection in color images using Markov random fields

Automatic video-based fire detection can greatly reduce fire alert delay in large industrial and commercial sites, at a minimal cost, by using the existing CCTV camera network. Most traditional computer vision methods for fire detection model the temporal dynamics of the flames, in conjunction with simple color filtering. An important drawback of these methods is that their performance degrades at lower framerates, and they cannot be applied to still images, limiting their applicability. Also, real-time operation often requires significant computational resources, which may be unfeasible for large camera networks. This paper presents a novel method for fire detection in static images, based on a Markov Random Field but with a novel potential function. The method detects 99.6% of fires in a large collection of test images, while generating less false positives then a state-of-the-art reference method. Additionally, parameters are easily trained on a 12-image training set with minimal user input