Andrzej Czyzewski

Multimedia Communications, Services and Security

An intelligent surveillance system based on visual information gathered by smart cameras, aimed at traffic monitoring with emphasis on traffic events caused by cars, is presented in the paper. The system components and their capabilities for automatic detection and recognition of selected parameters of cars, as well as different aspects of system efficiency, are described and discussed in detail. Smart facilities for Make and Model Recognition (MMR), License Plate Recognition (LPR) and Color Recognition (CR), embedded in the system in the form of their individual software implementations, are analyzed and their recognition rates detailed. Finally, a discussion of the systems efficiency as a whole, with an insight into possible future improvements, is included in the conclusion.

Intelligent Processing of Stuttered Speech

The process of counting stuttering events could be carried out more objectively through the automatic detection of stop-gaps, syllable repetitions and vowel prolongations. The alternative would be based on the subjective evaluations of speech fluency and may be dependent on a subjective evaluation method. Meanwhile, the automatic detection of intervocalic intervals, stop-gaps, voice onset time and vowel durations may depend on the speaker and the rules derived for a single speaker might be unreliable when trying to consider them as universal ones. This implies that learning algorithms having strong generalization capabilities could be applied to solve the problem. Nevertheless, such a system requires vectors of parameters, which characterize the distinctive features in a subjects speech patterns. In addition, an appropriate selection of the parameters and feature vectors while learning may augment the performance of an automatic detection system.The paper reports on automatic recognition of stuttered speech in normal and frequency altered feedback speech. It presents several methods of analyzing stuttered speech and describes attempts to establish those parameters that represent stuttering event. It also reports results of some experiments on automatic detection of speech disorder events that were based on both rough sets and artificial neural networks.

Detection and localization of selected acoustic events in acoustic field for smart surveillance applications

A method for automatic determination of position of chosen sound events such as speech signals and impulse sounds in 3-dimensional space is presented. The events are localized in the presence of sound reflections employing acoustic vector sensors. Human voice and impulsive sounds are detected using adaptive detectors based on modified peak-valley difference (PVD) parameter and sound pressure level. Localization based on signals from the multichannel acoustic vector probe is performed upon the detection. The described algorithms can be employed in surveillance systems to monitor behavior of public events participants. The results can be used to detect sound source position in real time or to calculate the spatial distribution of sound energy in the environment. Moreover, the spatial filtration can be performed to separate sounds arriving from a chosen direction.

Detection and Localization of Selected Acoustic Events in 3D Acoustic Field for Smart Surveillance Applications

A method for automatic determination of position of chosen sound events such as speech signals and impulse sounds in 3-dimensional space is presented. The events are localized in the presence of sound reflections employing acoustic vector sensors. Human voice and impulsive sounds are detected using adaptive detectors based on modified peak-valley difference (PVD) parameter and sound pressure level. Localization based on signals from the multichannel acoustic vector probe is performed upon the detection. The described algorithms can be useful in a surveillance systems to monitor the behavior of participants of public events. The results can be used to detect the position of sound source in real time or to calculate the spatial distribution of sounds in the environment. Moreover, spatial filtration can be performed to separate the sounds coming from the chosen direction.

Automatic identification of sound source position employing neural networks and rough sets

Methods for the identification of direction of the incoming acoustical signal in the presence of noise and reverberation are investigated. Since the problem is a non-deterministic one, thus applications of two learning algorithms, namely neural networks and rough sets are developed to solve it. Consequently, two sets of parameters have been formulated in order to discern target source from unwanted sound source position and then processed by learning algorithms. The applied feature extraction methods are discussed, training processes are described and obtained sound source localizing results are demonstrated and compared.

Multi-Stage Video Analysis Framework

The chapter is organized as follows. Section 2 presents the general structure of the proposed framework and a method of data exchange between system elements. Section 3 is describing the low-level analysis modules for detection and tracking of moving objects. In Section 4 we present the object classification module. Sections 5 and 6 describe specialized modules for detection and recognition of faces and license plates, respectively. In section 7 we discuss how video analysis results provided by other modules may be used for automatic detection of events related to possible security threats. The chapter ends with conclusions and discussion of future framework development.

Examining Kalman Filters Applied to Tracking Objects in Motion

Kalman filters were used for establishing relations between objects moving in video frames to the real moving objects under analysis. As a result of applying some popular methods of moving objects detection, the objects were represented by rectangles. A two-dimensional colour histogram based on a chromatic space was used for each object in experiments. The objects coupling with adequate regions including the relation of many-to-many was studied experimentally employing Kalman filters. The implemented algorithm provides a part of an advanced audio-video surveillance system for security applications.

Moving Object Detection and Tracking for the Purpose of Multimodal Surveillance System in Urban Areas

Background subtraction method based on mixture of Gaussians was employed to detect all regions in a video frame denoting moving objects. Kalman filters were used for establishing relations between the regions and real moving objects in a scene and for tracking them continuously. The objects were represented by rectangles. The objects coupling with adequate regions including the relation of many-to-many was studied experimentally employing Kalman filters. The implemented algorithm provides a part of an advanced audio-video surveillance system for security applications which is described briefly in the paper.

Neuro-rough control of masking thresholds for audio signal enhancement

Abstract The paper addresses the problem of neuro-rough hybridisation applied to digital processing of audio signals. Moreover, the application of some selected soft computing techniques to non-stationary noise reduction is described. Some attention is also put to a discussion of the intelligent decision algorithms performance. The noise reduction algorithm is based on the new perceptual approach exploiting some properties of the human auditory system. Furthermore, the paper introduces the engineered perceptual filter driven by an intelligent controller employing rules generated with the use of a rough set-based algorithm supported by a neural network. The goal of the intelligent controller is to estimate the current statistics of corrupting noise on the basis of the analysis of signals received from telecommunication channel. Thereafter, the noise estimate enables determining the masking threshold levels which allow making the noise inaudible in the audio signals. Since the implemented decision algorithm requires quantised data, thus the Kohonens self-organising maps (SOM) extended by various distance metrics were used as data quantisers. Some results of the experiments in the domain of non-stationary noise reduction in speech are discussed in the paper.

Vehicle classification based on soft computing algorithms

Experiments and results regarding vehicle type classification are presented. Three classes of vehicles are recognized: sedans, vans and trucks. The system uses a non-calibrated traffic camera, therefore no direct vehicle dimensions are used. Various vehicle descriptors are tested, including those based on vehicle mask only and those based on vehicle images. The latter ones employ Speeded Up Robust Features (SURF) and gradient images convolved with Gabor filters. Vehicle type is recognized with various classifiers: artificial neural network, K-nearest neighbors algorithm, decision tree and random forest.