Results of evaluation of the background subtraction algorithms implemented on a supercomputer platform in a parallel manner are presented in the article. The aim of the work is to chose an algorithm, a number of threads and a task scheduling method, that together provide satisfactory accuracy and efficiency of a real-time processing of high-resolution camera images, maintaining the cost of resources usage at a reasonable level. Two selected algorithms: the Gaussian mixture models and the Codebook, are presented and their computational complexity is discussed. Various approaches to the parallel implementation, including assigning the image pixels to threads, the task scheduling methods and the thread management systems, are presented. The experiments were performed on a supercomputer cluster, using a single machine with 12 physical cores. The accuracy and performance of the implemented algorithms were evaluated for varying image resolutions and numbers of concurrent processing threads. On a basis of the evaluation results, an optimal configuration for the parallel implementation of the system for real-time video content analysis on a supercomputer platform was proposed.

Innovative road signs that can autonomously display the speed limit in cases where the traffic situation requires it are under development. The autonomous road sign contains many types of sensors, of which the subject of interest in this article is the Doppler sensor that we have improved and the constructed and calibrated acoustic probe. An algorithm for performing vehicle detection and tracking, as well as vehicle speed measurement, in a signal acquired with a continuous wave Doppler sensor, is discussed. A method is also experimentally presented and studied for counting vehicles and for determining their movement direction by means of acoustic vector sensor application. The assumptions of the method employing spatial distribution of sound intensity determined with the help of an integrated three-dimensional (3D) sound intensity probe are discussed. The enhanced Doppler radar and the developed sound intensity probe were used for the experiments that are described and analyzed in the paper.

A method of detecting and counting road vehicles using an acoustic sensor placed by the road is presented. The sensor measures sound intensity in two directions: parallel and perpendicular to the road. The sound intensity analysis performs acoustic event detection. A normalized position of the sound source is tracked and used to determine if the detected event is related to a moving vehicle and to establish the direction of movement. The algorithm was tested on a continuous 24-h recording made in real-world conditions. The overall results were: recall 0.95, precision 0.95, F-score 0.95. In the analysis of one-hour slots, the worst results obtained in dense traffic were: recall 0.9, precision 0.93, F-score 0.91. The proposed method is intended for application in a network of traffic monitoring sensors, such as a smart city system. Its advantages include using a small, low cost and passive sensor, low algorithm complexity, and satisfactory detection accuracy.

A novel approach to detection of stationary objects in the video stream is presented. Stationary objects are these separated from the static background, but remaining motionless for a prolonged time. Extraction of stationary objects from images is useful in automatic detection of unattended luggage. The proposed algorithm is based on detection of image regions containing foreground image pixels having stable values in time and checking their correspondence with the detected moving objects. In the first stage of the algorithm, stability of individual pixels belonging to moving objects is tested using a model constructed from vectors. Next, clusters of pixels with stable color and brightness are extracted from the image and related to contours of the detected moving objects. This way, stationary (previously moving) objects are detected. False contours of objects removed from the background are also found and discarded from the analysis. The results of the algorithm may be analyzed further by the classifier, separating luggage from other objects, and the decision system for unattended luggage detection. The main focus of the paper is on the algorithm for extraction of stable image regions. However, a complete framework for unattended luggage detection is also presented in order to show that the proposed approach provides data for successful event detection. The results of experiments in which the proposed algorithm was validated using both standard datasets and video recordings from a real airport security system are presented and discussed.