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Automatic Video Surveillance System (AVSS) has become important to computer vision researchers as crime in public places has increased in the twenty first century. As a new branch of AVSS, baggage detection and classification has a broad area of security applications. Some of them are, detecting carriage of illegal materials into baggage, detecting unclaimed baggage in public space that can be placed...
High dimensionality of feature space is a problem in supervised machine learning. Redundant or superfluous features either slow down the training process or dilute the quality of classification. Many methods are available in literature for dimensionality reduction. Earlier studies explored a discernibility matrix (DM) based reduct calculation for dimensionality reduction. Discernibility matrix works...
For a safety critical task like driving, it is very important for the driver to be vigilant at all times. In this study, we explore a driver drowsiness monitoring and early warning system, which uses machine learning techniques based on vehicle telemetry data. The proposed system can ensure safe driving by real time monitoring of driving pattern. This proves to be a very cost effective technique over...
The lane marking detection task is an essential process in the field of semi-autonomous and autonomous navigation. This paper proposes a method that combines the color and edge information to robustly detect the lane marking within the image either located far on near to the vehicle. Firstly, the region of interest is extracted from the image. Secondly, the set of lane marking features are extracted...
This paper presents a research on classifying walking speed and step length simultaneously by using cerebral hemoglobin information. Nine healthy subjects performed walking task spontaneously in three levels of speed and three levels of step length. Brain information of the subjects was measured by using functional near-infrared spectroscopy (fNIRS) technology. The differences between the oxygenated...
Object classification is an important task in vision-based systems. In this work, an intelligent system to perform detection and classification of road objects is presented. The proposed method utilize machine learning algorithm to classify group of points into various categories that represent several road objects. This classification system was trained using 50 features of 2D laser point which were...
Recently, capabilities of many computer vision tasks have significantly improved due to advances in Convolutional Neural Networks. In our research, we demonstrate that it can be also used for face detection from low resolution thermal images, acquired with a portable camera. The physical size of the camera used in our research allows for embedding it in a wearable device or indoor remote monitoring...
This paper presents 2D image processing approach to playback detection in automatic speaker verification (ASV) systems using spectrograms as speech signal representation. Three feature extraction and classification methods: histograms of oriented gradients (HOG) with support vector machines (SVM), HAAR wavelets with AdaBoost classifier and deep convolutional neural networks (CNN) were compared on...
Vision based human fall action classification from non fall has been given significant importance over the past decade since the rise of falling events related to elderly people living alone has increased. This paper proposes a method to classify falls from non fall action in top Viewed kinect camera depth images. The usage of depth camera images provides an effective solution regarding privacy concerns...
Image stitching is an attractive method to merging multiple images. It can produce a wide-angle panoramic photograph while maintaining the quality of the source images. The process is simply performed by overlapping part of the images which contain common scene. Today, panoramic image stitching is widely used in applications such as 360-degree cameras and virtual reality photography. If the stitching...
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