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This uses combination of corner-based model and coefficients of undecimated wavelet packet transform (UWPT) for the proposed probabilistic Bayesian framework for object tracking. The UWPT coefficients are calculated for patch around each corner. The proposed scheme uses local descriptors e.g. UWPT coefficients, to improve global representation of object shape model. The proposed scheme then estimates...
In this paper we propose a novel approach to the detection of abandoned luggage in video surveillance. Candidates of abandoned luggage items which may pose potential security threats are first identified and localized by our proposed foreground-mask sampling technique. Our approach can deal with luggage pieces of arbitrary shape and color without the need for prior learning, and it works well under...
The paper proposes a new holistic representation for actions, called action signature, which uses both global and local motion variations to classify an action. The action signatures are generated by estimating a sequence of orientations from the motion history images. A mixture of von Mises distributions is used in the classification stage of the system.
The paper presents a probabilistic Bayesian framework for object tracking using a combination of a corner-based model and coefficients of Undecimated wavelet packet transform (UWPT) inside a patch around each corner. This combination uses the UWPT coefficients patch helps to enrich the global representation of the object shape model by local descriptors. The goal is to maximize the posterior of the...
This paper describes a method for action recognition using a classification algorithm based on mixtures of von Mises distributions processing action signatures. An action signature is a ID sequence of angles, forming a trajectory, which are extracted from a 2D map of adjusted orientations (subtracting the average orientation) of the gradient of the motion-history image. To obtain the action signature,...
This paper presents a very good approach for tracking through occlusion by applying a probabilistic model to tracking features on an object. The authors present that the method works well even through partial occlusions.
This paper proposes an approach to abandoned luggage detection that mimics human behavior in monitoring a scene: first the abandoned luggage is detected through a foreground-mask sampling; then people nearby the detected object are tracked to eventually associate the left object with its possessor; ultimately, MAP principle is applied to perform reasoning.
This work deals with textural segmenting of high resolution sidescan sonar images by using active contours and Gabor filters. In fact this method is a modification of Chan and Vese Active contour model. It makes the method suitable for textural segmenting of above said images. First, image is passed through a symmetric bank of Gabor filters. Then, filtered images that possess a significant component...
Moving object classification in far-field video is a key component of smart surveillance systems. In this paper, we propose a reliable system for person-vehicle classification which works well in challenging real-word conditions, including the presence of shadows, low resolution imagery, perspective distortions, arbitrary camera viewpoints, and groups of people. Our system runsin real-time (30 Hz)...
Gaussian mixture models (GMM) is used to represent the dynamic background in a surveillance video to detect the moving objects automatically. All the existing GMM based techniques inherently use the proportion by which a pixel is going to observe the background in any operating environment. In this paper we first show that such a proportion not only varies widely across different scenarios but also...
In this paper we present a people tracking algorithm which is able to detect and track soccer players in complex situations with varying light conditions, high frame rate, and real time processing. Object segmentation is performed by means of an algorithm based on background subtraction. In order to cope with presence of moving objects and light changes during the background modeling phase, an approach...
In this paper, we propose a shadow removal algorithm for indoor scenes. This algorithm uses three types of constraints: chromaticity consistency, texture consistency and range of shadow intensity. The chromaticity consistency is verified in both HSV and RGB color spaces. The texture verification is based on the local coherency (over a pixel neighbourhood) of intensity reduction ratio between shadows...
This paper describes a simple and novel algorithm for detecting foreground objects in video sequences using just two consecutive frames. The method isdivided in three layers: the perceptual layer detects regions of interest by looking for pixels whose values change significantly between the two frames. The memory layer allows for the detection of objects that may become stationary (these objects are...
In this paper, we present retrieval methods for extracting colored moving objects from surveillance cameras. In particular, we describe a method to classify moving objects into one of six colors. The method includes two sets of parameters. The first set can be used to compensate for illumination conditions and camera differences. The second set is used to tune the color extraction for specific object...
The technique proposed in this paper combines three algorithms for shadow removal in indoor scenes. First algorithm takes an advantage of the assumption on the chromaticity consistency: the ambient light chromaticity is approximately the same as the chromaticity of the diffuse light. Magnitude of differences between chromaticity values of shadow and non-shadow regions for different hue values are...
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