HMM based method for dynamic texture detection

A method for detection of dynamic textures in video is proposed. It is observed that the motion vectors of most of the dynamic textures (e.g. sea waves, swaying tree leaves and branches in the wind, etc.) exhibit random motion. On the other hand, regular motion of ordinary video objects has well-defined directions. In this paper, motion vectors of moving objects are estimated and tracked based on a minimum distance based metric. The direction of the motion vectors are then quantized to define two threestate Markov models corresponding to dynamic textures and ordinary moving objects with consistent directions. Hidden Markov Models (HMMs) are used to classify the moving objects in the final step of the algorithm

Shadow detection using 2D cepstrum

Shadows constitute a problem in many moving object detection and tracking algorithms in video. Usually, moving shadow regions lead to larger regions for detected objects. Shadow pixels have almost the same chromaticity as the original background pixels but they only have lower brightness values. Shadow regions usually retain the underlying texture, surface pattern, and color value. Therefore, a shadow pixel can be represented as a.x where x is the actual background color vector in 3-D RGB color space and a is a positive real number less than 1. In this paper, a shadow detection method based on two-dimensional (2-D) cepstrum is proposed. © 2009 SPIE

Wavelet based detection of moving tree branches and leaves in video

A method for detection of tree branches and leaves in video is proposed. It is observed that the motion vectors of tree branches and leaves exhibit random motion. On the other hand regular motion of green colored objects has well-defined directions. In this paper, the wavelet transform of motion vectors are computed and objects are classified according to the wavelet coefficients of motion vectors. Color information is also used to reduce the search space in a given image frame of the video. Motion trajectories of moving objects are modeled as Markovian processes and Hidden Markov Models (HMMs) are used to classify the green colored objects in the final step of the algorithm. © 2006 IEEE

Falling person detection using multisensor signal processing

Falls are one of the most important problems for frail and elderly people living independently. Early detection of falls is vital to provide a safe and active lifestyle for elderly. Sound, passive infrared (PIR) and vibration sensors can be placed in a supportive home environment to provide information about daily activities of an elderly person. In this paper, signals produced by sound, PIR and vibration sensors are simultaneously analyzed to detect falls. Hidden Markov Models are trained for regular and unusual activities of an elderly person and a pet for each sensor signal. Decisions of HMMs are fused together to reach a final decision

Two-dimensional Mellin and mel-cepstrum for image feature extraction

An image feature extraction method based on two-dimensional (2D)Mellin cepstrum is introduced. The concept of one-dimensional (1D) melcepstrum which is widely used in speech recognition is extended to two-dimensions both using the ordinary 2D Fourier Transform and the Mellin transform in this article. The resultant feature matrices are applied to two different classifiers (Common Matrix Approach and Support Vector Machine) to test the performance of the melcepstrum and Mellincepstrum based features. Experimental studies indicate that recognition rates obtained by the 2D melcepstrum based method are superior to the recognition rates obtained using 2D PCA and ordinary image matrix based face recognition in both classifiers. © 2011 Springer Science+Business Media B.V

VOC gas leak detection using pyro-electric infrared sensors

In this paper, we propose a novel method for detecting and monitoring Volatile Organic Compounds (VOC) gas leaks by using a Pyro-electric (or Passive) Infrared (PIR) sensor whose spectral range intersects with the absorption bands of VOC gases. A continuous time analog signal is obtained from the PIR sensor. This signal is discretized and analyzed in real time. Feature parameters are extracted in wavelet domain and classified using a Markov Model (MM) based classifier. Experimental results are presented. ©2010 IEEE

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

Silhouette-based method for object classification and human action recognition in video

In this paper we present an instance based machine learning algorithm and system for real-time object classification and human action recognition which can help to build intelligent surveillance systems. The proposed method makes use of object silhouettes to classify objects and actions of humans present in a scene monitored by a stationary camera. An adaptive background subtracttion model is used for object segmentation. Template matching based supervised learning method is adopted to classify objects into classes like human, human group and vehicle; and human actions into predefined classes like walking, boxing and kicking by making use of object silhouettes. © Springer-Verlag Berlin Heidelberg 2006