Radosław Hofman

SmartMonitor--an intelligent security system for the protection of individuals and small properties with the possibility of home automation.

“SmartMonitor” is an intelligent security system based on image analysis that combines the advantages of alarm, video surveillance and home automation systems. The system is a complete solution that automatically reacts to every learned situation in a pre-specified way and has various applications, e.g., home and surrounding protection against unauthorized intrusion, crime detection or supervision over ill persons. The software is based on well-known and proven methods and algorithms for visual content analysis (VCA) that were appropriately modified and adopted to fit specific needs and create a video processing model which consists of foreground region detection and localization, candidate object extraction, object classification and tracking. In this paper, the “SmartMonitor” system is presented along with its architecture, employed methods and algorithms, and object analysis approach. Some experimental results on system operation are also provided. In the paper, focus is put on one of the aforementioned functionalities of the system, namely supervision over ill persons.

Application of foreground object patterns analysis for event detection in an innovative video surveillance system

SmartMonitor is an innovative surveillance system based on video content analysis. It is a modular solution that can work in several predefined scenarios mainly concerned with home/surrounding protection against unauthorized intrusion, supervision over ill person and crime detection. Each scenario is associated with several actions and conditions, which imply the utilization of algorithms with various input parameters. In this paper, focus is put on the analysis of foreground object patterns for the purposes of event recognition, as well as the experimental investigation of selected methods and algorithms which were developed and employed for the SmartMonitor system prototype. The prototype performs three main tasks: detection and localization of foreground regions using adaptive background modelling based on Gaussian Mixture Models, candidate objects extraction and classification using Haar and HOG descriptors, and tracking using Mean-Shift algorithm. The main goal of the work described here is to match system parameters with each scenario to provide the highest effectiveness and to decrease the number of false alarms.

Similarity Estimation of Textile Materials Based on Image Quality Assessment Methods

In this paper some experimental results obtained by the application of various image quality assessment methods for the estimation of similarity of textile materials are presented. Such approach is considered as a part of an artificial intelligence based system developed for the recognition of clothing styles based on multi-dimensional analysis of descriptors and features.

Extraction of the Foreground Regions by Means of the Adaptive Background Modelling Based on Various Colour Components for a Visual Surveillance System

Intelligent monitoring systems based on visual content analysis are often composed of three main modules — background modelling, object extraction and object tracking. This paper describes a method for adaptive background modelling utilizing Gaussian Mixture Models (GMM) and various colour components. The description is based on the experimental results obtained during the development of the SmartMonitor — an innovative security system based on video content analysis. In this paper the main characteristics of the system are introduced. An explanation of GMM algorithm and a presentation of its main advantages and drawbacks is provided. Finally, some experimentally obtained images containing foreground regions extracted with the use of various background models are presented.

The Removal of False Detections from Foreground Regions Extracted Using Adaptive Background Modelling for a Visual Surveillance System

For recent surveillance systems, the false detection removal process is an important step which succeeds the extraction of foreground regions and precedes the classification of object silhouettes. This paper describes the false object removal process when applied to the ’SmartMonitor’ system — i.e. an innovative monitoring system based on video content analysis that is currently being developed to ensure the safety of people and assets within small areas. This paper firstly briefly describes the basic characteristics and advantages of the system. A description of the methods used for background modelling and foreground extraction is also given. The paper then goes on to explain the artefacts removal process using various background models. Finally the paper presents some experimental results alongside a concise explanation of them.

Automatic Analysis of Vehicle Trajectory Applied to Visual Surveillance

In this paper we discuss a problem of automatic analysis of vehicles trajectories in the context of illegal movements. It is crucial to detect restricted or security critical behaviour on roads, especially for safety protection and fluent traffic. Here, we propose an vision-based algorithm for vehicle detection and tracking, which is later employed to recognize patterns in resultant trajectories. Experiments were performed on real video streams. They gave encouraging results.

Applying Image Features and AdaBoost Classification for Vehicle Detection in the ‘SM4Public’ System

The main goal of works described in the paper is to test and select algorithms to be implemented in the ‘SM4Public’ security system for public spaces. The paper describes the use of cascading approaches in the scenario concerning the detection of vehicles in static images. Three feature extractors were used along with benchmark datasets in order to prepare eight various cascades of classifiers. The algorithms selected for feature extraction are Histogram of Oriented Gradients, Local Binary Patterns and Haar-like features. AdaBoost was used as a classifier. The paper briefly introduces the ‘SM4Public’ system characteristics, characterizes the employed algorithms and presents sample experimental results.

Finding Similar Clothes Based on Semantic Description for the Purpose of Fashion Recommender System

The fashion domain has been one of the most growing areas of e-commerce, hence the issue of facilitating cloth searching in fashion-related websites becomes an important topic of research. The paper deals with measuring the similarity between items of clothing and between complete outfits, based on the semantic description prepared by users and experts according to a previously developed fashion ontology. Proposed approach deals with different types of attributes describing clothes and allows for calculating similarity between the whole outfits in a domain-aware manner. Exemplary results of experiments performed on real clothing datasets are presented.

Detecting Parked Vehicles in Static Images Using Simple Spectral Features in the ‘SM4Public’ System

In the paper, the use of selected algorithms for the detection of specific objects and extraction of their characteristics from static images is presented. The problem concerns the selection of algorithms to be implemented in the ‘SM4Public’ security system for public spaces and is focused on specific system working scenario: detecting vehicles parked in restricted areas. Two popular feature extractors based on the Discrete Cosine Transform and Discrete Fourier Transform were experimentally tested. The paper contains the description of the ‘SM4Public’ system, explanation of the problem and presentation of similar solutions given in the literature. The stress is put on the definition of the employed feature extractors and the description of the experimental results.

Clustering-Based Retrieval of Similar Outfits Based on Clothes Visual Characteristics

The fashion domain has been one of the most growing areas of e-commerce, hence the issue of facilitating cloth searching in fashionrelated websites becomes an important topic of research. The paper deals with searching for similar outfits in the clothing images database, using information extracted from unconstrained images containing human silhouettes. Medoids-based clustering is introduced in order to detect groups of similar outfits and speed up the retrieval procedure. Exemplary results of experiments performed on real clothing datasets are presented.