V. A. Ivanov

Real-time detection of moving objects from a sequence of images

A new algorithm of estimating the stationary part of the background of the observed scene and its adaptation to changes in this scene and observation conditions are proposed. The algorithm is optimal in the statistical sense and employs the minimum number of statistical characteristics (moving average, variance, and median), which allows their recurrent calculation necessary for real-time implementation. The validity of the proposed method is confirmed by results of a computational experiment with real video sequences.

Space-time analysis of video sequences for detecting abandoned objects

A new algorithm for detecting abandoned objects is proposed. The algorithm is based on analyzing the space and time characteristics of the overall background, background consisting of stationary objects, and current image of the video sequence. The algorithm can operate under conditions of short-time overlapping of the abandoned object, has a moderate computational complexity, and is designed for application in video surveillance systems.

Formation of a high-resolution image from a series of mutually shifted images using optimal linear prediction

A high-resolution image is constructed by joint interpolation of a series of low-resolution images differing in mutual shifts which are not multiples of the sampling interval. Interpolation coefficients are determined by using correlations between the readings of the initial images. Results of experiments showing the efficiency of the proposed approach are given.

Model of a persistent photodetector for analyzing the threshold sensitivity in the problem of detection of a moving point object

A model of the direction-finding characteristic of a moving point object for a persistent photodetector is proposed. The model ensures the calculation of the direction-finding characteristic as a function of photodetector parameters and object velocity, which form the basis for calculating the probability of detection of moving point objects. Results of numerical experiments are reported.

Specific features of operation of fallen person detection algorithms based on a sequence of scene images

Specific features of operation of fallen person detection algorithms having a moderate computational complexity and based on a sequence of images obtained by stationary video surveillance cameras are analyzed. Approaches to enhancing the algorithm operation stability are proposed. One variant of obtaining a more reliable silhouette of the object is considered; parameters used in recognition of the object states are analyzed and selected. A method of increasing the detector stability is proposed. This method is based on identifying intervals of stability of the average values of parameters, which allows reliability of detection of the object states to be improved. Results of computational experiments are presented.

Modeling and analysis of motion of biological objects on the basis of a sequence of images obtained in studying the motion activity

A new approach to analyzing the data obtained by tracking biological objects in studying the motion activity is considered. The approach is based on a proposed mathematical model of motion of a biological object. The object behavior observed during the experiment can be described in the form of model parameters. A numerical experiment with model data is performed, which shows that the estimates predicted by the proposed method ensure a smaller root-mean-square deviation of the object state classification than other available methods.

Fast algorithms of image construction with a high sampling frequency

Some modifications of the algorithm of image construction with a high sampling frequency are proposed. These modifications multiply reduce the computational costs with an almost unchanged quality of the result. Software implementation of the proposed modifications of the algorithm allows the latter to be used in video surveillance systems for real-time formation of high-quality image fragments.

Specific features of detecting point objects in images formed by a detector array

The efficiency of detecting point objects in images obtained by a photodetector array is analyzed. It is shown that the detection efficiency depends both on the image processing method and on the relationship between the sizes of the array elements, the gaps between these elements, and the point spread function of the optical system forming the image. Results of a computational experiment are given, which confirm the existence of an optimal relationship between these parameters.

Optimal Linear Prediction in Interelement Interpolation Problems of Discrete Signals and Images

Optimal linear prediction (OLP) as applied to form oversampled signals from undersampled signals is considered. The OLP-based algorithm for aliasing suppression and sampling frequency enhancement of an image that accumulates a series of geometrically distorted undersampled images is proposed. Results of numerical experiments showing the significant quality improvement for images constructed by OLP are given.

Defect correction in images from a scanline detector

IR image defects (faults, overshoots, and a strong additive periodic noise) are analyzed for an autonomous observation system with an unstable radio channel communication.