Alessia Saggese

Pattern recognition in stained HEp-2 cells: Where are we now?

Indirect Immunouorescence (IIF) images are increasingly being used for the diagnosis of autoimmune diseases. However, the analysis of this kind of images has until now reached a comparatively low level of automation, if compared with other medical imaging techniques. The Special Issue on the Analysis and Recognition of Indirect Immunouorescence Images of the Pattern Recognition journal aims at providing a comprehensive evaluation of the state of the art for the staining pattern classication problem, through the adoption of a common experimental protocol and the testing of all the methods on a publicly available dataset. This paper will present both a survey of the articles accepted for the special issue, highlighting their original aspects, and a detailed comparison and discussion of the corresponding experimental results, in order to assess which are the advantages and disadvantages of each approach.

A real time algorithm for people tracking using contextual reasoning

Abstract In this paper we present a real-time tracking algorithm that is able to deal with complex occlusions involving a plurality of moving objects simultaneously. The rationale is grounded on a suitable representation and exploitation of the recent history of each single moving object being tracked. The object history is encoded using a state, and the transitions among the states are described through a Finite State Automata (FSA). In presence of complex situations the tracking is properly solved by making the FSA’s of the involved objects interact with each other. This is the way for basing the tracking decisions not only on the information present in the current frame, but also on conditions that have been observed more stably over a longer time span. The object history can be used to reliably discern the occurrence of the most common problems affecting object detection, making this method particularly robust in complex scenarios. An experimental evaluation of the proposed approach has been made on two publicly available datasets, the ISSIA Soccer Dataset and the PETS 2010 database.

Reliable detection of audio events in highly noisy environments

The authors propose an audio events detection system tailored to surveillance applications.The method has been tested on a huge and challenging data set, made publicly available.The performance analysis has been done for low SNR values and under various conditions.A comparative analysis with other methods from the literature has been performed. In this paper we propose a novel method for the detection of audio events for surveillance applications. The method is based on the bag of words approach, adapted to deal with the specific issues of audio surveillance: the need to recognize both short and long sounds, the presence of a significant noise level and of superimposed background sounds of intensity comparable to the audio events to be detected. In order to test the proposed method in complex, realistic scenarios, we have built a large, publicly available dataset of audio events. The dataset has allowed us to evaluate the robustness of our method with respect to varying levels of the Signal-to-Noise Ratio; the experimentation has confirmed its applicability under real world conditions, and has shown a significant performance improvement with respect to other methods from the literature.

Exploiting the deep learning paradigm for recognizing human actions.

In this paper we propose a novel method for recognizing human actions by exploiting a multi-layer representation based on a deep learning based architecture. A first level feature vector is extracted and then a high level representation is obtained by taking advantage of a Deep Belief Network trained using a Restricted Boltzmann Machine. The classification is finally performed by a feed-forward neural network. The main advantage behind the proposed approach lies in the fact that the high level representation is automatically built by the system exploiting the regularities in the dataset; given a suitably large dataset, it can be expected that such a representation can outperform a hand-design description scheme. The proposed approach has been tested on two standard datasets and the achieved results, compared with state of the art algorithms, confirm its effectiveness.

Audio surveillance using a bag of aural words classifier

In this paper we propose a novel approach for the audio-based detection of events. The approach adopts the bag of words paradigm, and has two main advantages over other techniques present in the literature: the ability to automatically adapt (through a learning phase) to both short, impulsive sounds and long, sustained ones, and the ability to work in noisy environments where the sounds of interest are superimposed to background sounds possibly having similar characteristics. The proposed method has been experimentally validated on a large database of sounds, including several kinds of background noise, which are superimposed to the sounds to be recognized. The obtained performance has been compared with the results of another audio event detection algorithm from the literature, showing a significant improvement.

Audio Surveillance of Roads: A System for Detecting Anomalous Sounds

In the last decades, several systems based on video analysis have been proposed for automatically detecting accidents on roads to ensure a quick intervention of emergency teams. However, in some situations, the visual information is not sufficient or sufficiently reliable, whereas the use of microphones and audio event detectors can significantly improve the overall reliability of surveillance systems. In this paper, we propose a novel method for detecting road accidents by analyzing audio streams to identify hazardous situations such as tire skidding and car crashes. Our method is based on a two-layer representation of an audio stream: at a low level, the system extracts a set of features that is able to capture the discriminant properties of the events of interest, and at a high level, a representation based on a bag-of-words approach is then exploited in order to detect both short and sustained events. The deployment architecture for using the system in real environments is discussed, together with an experimental analysis carried out on a data set made publicly available for benchmarking purposes. The obtained results confirm the effectiveness of the proposed approach.

Recognizing Human Actions by a Bag of Visual Words

In this paper a novel method for action recognition based on the bag of visual words approach is proposed. The main contribution is to model each action through a high level features vector computed as the histogram of the visual words: the visual words are extracted by analyzing global descriptors of the scene and their occurrences are evaluated according to a codebook, a kind of dictionary, which encodes the typical visual words, automatically extracted during the learning phase. The classification is performed by using an SVM classifier, trained only by using high level features vectors, in order to increase the overall reliability of the system. The experimentation has been conducted over two recently proposed datasets, the MIVIA and the MHAD, the promising results confirm the robustness and the stability of the proposed approach.

Real-Time Fire Detection for Video-Surveillance Applications Using a Combination of Experts Based on Color, Shape, and Motion

In this paper, we propose a method that is able to detect fires by analyzing videos acquired by surveillance cameras. Two main novelties have been introduced. First, complementary information, based on color, shape variation, and motion analysis, is combined by a multiexpert system. The main advantage deriving from this approach lies in the fact that the overall performance of the system significantly increases with a relatively small effort made by the designer. Second, a novel descriptor based on a bag-of-words approach has been proposed for representing motion. The proposed method has been tested on a very large dataset of fire videos acquired both in real environments and from the web. The obtained results confirm a consistent reduction in the number of false positives, without paying in terms of accuracy or renouncing the possibility to run the system on embedded platforms.

Combining Neural Networks and Fuzzy Systems for Human Behavior Understanding

The psychological overcharge issue related to human inadequacy to maintain a constant level of attention in simultaneously monitoring multiple visual information sources makes necessary to develop enhanced video surveillance systems that automatically understand human behaviors and identify dangerous situations. This paper introduces a semantic human behavioral analysis (HBA) system based on a neuro-fuzzy approach that, independently from the specific application, translates tracking kinematic data into a collection of semantic labels characterizing the behavior of different actors in a scene in order to appropriately classify the current situation. Different from other HBA approaches, the proposed system shows high level of scalability, robustness and tolerance for tracking imprecision and, for this reason, it could represent a valid choice for improving the performance of current systems.

An Ensemble of Rejecting Classifiers for Anomaly Detection of Audio Events

Audio analytic systems are receiving an increasing interest in the scientific community, not only as stand alone systems for the automatic detection of abnormal events by the interpretation of the audio track, but also in conjunction with video analytics tools for enforcing the evidence of anomaly detection. In this paper we present an automatic recognizer of a set of abnormal audio events that works by extracting suitable features from the signals obtained by microphones installed into a surveilled area, and by classifying them using two classifiers that operate at different time resolutions. An original aspect of the proposed system is the estimation of the reliability of each response of the individual classifiers. In this way, each classifier is able to reject the samples having an overall reliability below a threshold. This approach allows our system to combine only reliable decisions, so increasing the overall performance of the method. The system has been tested on a large dataset of samples acquired from real world scenarios, the audio classes of interests are represented by gunshot, scream and glass breaking in addition to the background sounds. The preliminary results obtained encourage further research in this direction.