Moving Object Detection in Wavelet Compressed Video

Cataloged from PDF version of article.In many surveillance systems the video is stored in wavelet compressed form.In this paper, an algorithm for moving object and region detection in video which is compressed using a wavelet transform (WT) is developed.The algorithm estimates the WT of the background scene from the WTs of the past image frames of the video.The WT of the current image is compared with the WT of the background and the moving objects are determined from the difference.The algorithm does not perform inverse WT to obtain the actual pixels of the current image nor the estimated background. This leads to a computationally efficient method and a system compared to the existing motion estimation methods. (C) 2005 Published by Elsevier B.V

Entropy-Functional-Based Online Adaptive Decision Fusion Framework with Application to Wildfire Detection in Video

Cataloged from PDF version of article.In this paper, an entropy-functional-based online adaptive decision fusion (EADF) framework is developed for image analysis and computer vision applications. In this framework, it is assumed that the compound algorithm consists of several subalgorithms, each of which yields its own decision as a real number centered around zero, representing the confidence level of that particular subalgorithm. Decision values are linearly combined with weights that are updated online according to an active fusion method based on performing entropic projections onto convex sets describing subalgorithms. It is assumed that there is an oracle, who is usually a human operator, providing feedback to the decision fusion method. A video-based wildfire detection system was developed to evaluate the performance of the decision fusion algorithm. In this case, image data arrive sequentially, and the oracle is the security guard of the forest lookout tower, verifying the decision of the combined algorithm. The simulation results are presented

Wavelet based flickering flame detector using differential PIR sensors

Cataloged from PDF version of article.A Pyro-electric Infrared (PIR) sensor based flame detection system is proposed using a Markovian decision algorithm. A differential PIR sensor is only sensitive to sudden temperature variations within its viewing range and it produces a time-varying signal. The wavelet transform of the PIR sensor signal is used for feature extraction from sensor signal and wavelet parameters are fed to a set of Markov models corresponding to the flame flicker process of an uncontrolled fire, ordinary activity of human beings and other objects. The final decision is reached based on the model yielding the highest probability among others. Comparative results show that the system can be used for fire detection in large rooms. (C) 2012 Elsevier Ltd. All rights reserved

Video fire detection - Review

Cataloged from PDF version of article.This is a review article describing the recent developments in Video based Fire Detection (VFD). Video surveillance cameras and computer vision methods are widely used in many security applications. It is also possible to use security cameras and special purpose infrared surveillance cameras for fire detection. This requires intelligent video processing techniques for detection and analysis of uncontrolled fire behavior. VFD may help reduce the detection time compared to the currently available sensors in both indoors and outdoors because cameras can monitor “volumes” and do not have transport delay that the traditional “point” sensors suffer from. It is possible to cover an area of 100 km2 using a single pan-tiltzoom camera placed on a hilltop for wildfire detection. Another benefit of the VFD systems is that they can provide crucial information about the size and growth of the fire, direction of smoke propagation. © 2013 Elsevier Inc. All rights reserve

LMS based adaptive prediction for scalable video coding

3D video codecs have attracted recently a lot of attention, due to their compression performance comparable with that of state-of-art hybrid codecs and due to their scalability features. In this work, we propose a least mean square (LMS) based adaptive prediction for the temporal prediction step in lifting implementation. This approach improves the overall quality of the coded video, by reducing both the blocking and ghosting artefacts. Experimental results show that the video quality as well as PSNR values are greatly improved with the proposed adaptive method, especially for video sequences with large contrast between the moving objects and the background and for sequences with illumination variations. © 2006 IEEE

Linear and nonlinear temporal prediction employing lifting structures for scalable video coding

Scalable 3D video codecs based on wavelet lifting structures have attracted recently a lot of attention, due to their compression performance comparable with that of state-of-art hybrid codecs. In this work, we propose a set of linear and nonlinear predictors for the temporal prediction step in lifting implementation. The predictor uses pixels on the motion trajectories of the frames in a window around the pixel to be predicted to improve the quality of prediction. Experimental results show that the video quality as well as PSNR values are improved with the proposed prediction method