Survey Paper of Approaches for Real Time Fire Detection

Accidental fire always causes great loss. If that fire is detected in time, then loss can be minimized. Hence there should be more efficient systems to avoid losses. Most of the fire detection systems are based on sensors. These sensors give false alarms in case of cigarette or essence sticks are burnt and these systems are also quite costly. By using fire detection system through video surveillance cameras the cost of system can be reduced. The videos achieved by popular surveillance cameras are analysed and different topologies of information, respectively based on colour and movement are united into a multi expert system in order to increase the overall accuracy of the approach, making it possible its usage in real time applications. The systems use HSV, HSL, YUV models. In these systems, the models are based on colour, motion and shape. The approaches have been tested on a wide database with the aim of assessing its performance both in terms of sensitivity and specificity

Real-time face analysis for gender recognition on video sequences

2016 - 2017This research work has been produced with the aim of performing gender recognition in real-time on face images extracted from real video sequences. The task may appear easy for a human, but it is not so simple for a computer vision algorithm. Even on still images, the gender recognition classifiers have to deal with challenging problems mainly due to the possible face variations, in terms of age, ethnicity, pose, scale, occlusions and so on. Additional challenges have to be taken into account when the face analysis is performed on images acquired in real scenarios with traditional surveillance cameras. Indeed, the people are unaware of the presence of the camera and their sudden movements, together with the low quality of the images, further stress the noise on the faces, which are affected by motion blur, different orientations and various scales. Moreover, the need of providing a single classification of a person (and not for each face image) in real-time imposes to design a fast gender recognition algorithm, able to track a person in different frames and to give the information about the gender quickly. The real-time constraint acquires even more relevance considering that one of the goals of this research work is to design an algorithm suitable for an embedded vision architecture. Finally, the task becomes even more challenging since there are not standard benchmarks and protocols for the evaluation of gender recognition algorithms. In this thesis the attention has been firstly concentrated on the analysis of still images, in order to understand which are the most effective features for gender recognition. To this aim, a face alignment algorithm has been applied to the face images so as to normalize the pose and optimize the performance of the subsequent processing steps. Then two methods have been proposed for gender recognition on still images. First, a multi-expert which combines the decisions of classifiers fed with handcrafted features has been evaluated. The pixel intensity values of face images, namely the raw features, the LBP histograms and the HOG features have been used to train three experts which takes their decision by taking into account, respectively, the information about color, texture and shape of a human face. The decisions of the single linear SVMs have been combined with a weighted voting rule, which demonstrated to be the most effective for the problem at hand. Second, a SVM classifier with a chi-squared kernel based on trainable COSFIRE filters has been fused with an expert which rely on SURF features extracted in correspondence of certain facial landmarks. The complementarity of the two experts has been demonstrated and the decisions have been combined with a stacked classification scheme. An experimental evaluation of all the methods has been carried out on the GENDER-FERET and the LFW datasets with a standard protocol, so allowing the possibility to perform a fair comparison of the results. Such evaluation proved that the couple COSFIRE-SURF is the one which achieves the best accuracy in all the cases (accuracy of 94.7% on GENDER-FERET and 99.4% on LFW), even compared with other state of the art methods. Anyway, the performance achieved by the multi-expert which rely on the fusion of RAW, LBP and HOG classifiers can also be considered very satisfying (accuracy of 93.0% on GENDER-FERET and 98.4% on LFW)...[edited by Author]XXX cicl

A Data-Flow Middleware Platform for Real-Time Video Analysis

2015 - 2016In this thesis we introduce a new software platform for the development of real-time video analysis applications, that has been designed to simplify the realization and the deployment of intelligent video-surveillance systems. The platform has been developed following the Plugin Design Pattern: there is an applicationindependent middleware, providing general purpose services, and a collection of dynamically loaded modules (plugins) carrying out domain-specific tasks. Each plugin defines a set of node types, that can be instantiated to form a processing network, according to the data-flow paradigm: the control of the execution flow is not wired in the application-specific code but is demanded to the middleware, which activates each node as soon as its inputs are available and a processor is ready. A first benefit of this architecture is its impact on the software development process: the plugins are loosely coupled components that are easier to develop and test, and easier to reuse in a different project. A second benefit, due to the shift of the execution control to the middleware, is the performance improvement, since the middleware can automatically parallelize the processing using the available processors or cores, as well as using the same information or data for different thread of execution. In order to validate the proposed software architecture, in terms of both performance and services provided by the middleware, we have undertaken the porting to the new middleware of two novel intelligent surveillance applications, by implementing all the nodes required by the algorithms. The first application is an intelligent video surveillance system based on people tracking algorithm. The application uses a single, fixed camera; on the video stream produced by the camera, background subtraction is performed (with a dynamically updated background) to detect foreground objects. These objects are tracked, and their trajectories are used to detect events of interest, like entering a forbidden area, transiting on a one-way passage in the wrong direction, abandoning objects and so on. The second application integrated is a fire detection algorithm, which combines information based on color, shape and movement in order to detect the flame. Two main novelties have been introduced: first, complementary information, respectively based on color, shape variation and motion analysis, are combined by a multi expert 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 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. [edited by author]In questa tesi introduciamo una nuova piattaforma software per lo sviluppo di applicazioni di video analisi, progettato per semplificare lo sviluppo e la messa in opera di un sistema di video analisi intelligente. La piattaforma è stata sviluppata seguendo il Design Pattern Plugin: c’è un middleware indipendente dalla piattaforma che mette a disposizione servizi per vari scopi, ed una collezione di moduli caricati dinamicamente (plugin) per la risoluzione di specifici task. Ogni plugin definisce un set di tipi di nodi, che possono essere istanziati per formare una rete di elaborazione, in accordo al paradigma data-flow: Il controllo del flusso di esecuzione non è cablato nel codice specifico dell'applicazione ma viene richiesto al middleware che attiva ogni nodo non appena i suoi ingressi sono disponibili e un processore è pronto. Un primo vantaggio di questa architettura è il suo impatto sul processo di sviluppo del software: i plugin sono componenti poco accoppiati che sono più facili da sviluppare e testare e più facilmente riutilizzabili in un altro progetto. Un secondo beneficio, dovuto allo spostamento del controllo di esecuzione al middleware, è il miglioramento delle prestazioni, dal momento che il middleware può automaticamente parallelizzare l'elaborazione utilizzando i processori o i core disponibili, nonché utilizzando le stesse informazioni o dati per differenti thread di esecuzione . Al fine di convalidare l'architettura software proposta, sia in termini di prestazioni che di servizi forniti dal middleware, è stato effettuato il porting all’interno del middleware di due applicazioni di sorveglianza intelligenti, implementando tutti i nodi richiesti dagli algoritmi. La prima applicazione è un sistema di videosorveglianza intelligente basato su un algoritmo di tracking delle persone. L'applicazione utilizza una singola telecamera fissa; sul flusso video prodotto dalla telecamera viene eseguita una sottrazione del background (con un aggiornamento dinamicamente del backgroung) per rilevare oggetti di foreground. Questi oggetti vengono tracciati e le loro traiettorie vengono utilizzate per rilevare eventi di interesse, come accesso in una zona proibita, oggetti abbandonati e così via. La seconda applicazione integrata è un algoritmo di rilevazione del fuoco che combina informazioni basate su colore, forma e movimento per rilevare le fiamme. Sono state introdotte due novità principali: in primo luogo, informazioni complementari, rispettivamente basate sul colore, sulla variazione di forma e sull'analisi del movimento, sono combinate tra loro da un sistema multi-esperto. Il vantaggio principale derivante da questo approccio risiede nel fatto che le prestazioni complessive del sistema aumentano significativamente con uno sforzo relativamente piccolo. In secondo luogo, un innovativo descrittore basato su un approccio "bag-of-words" per rappresentare il movimento. Il metodo proposto è stato testato su un grande dataset di video acquisiti sia in ambienti reali che dal web. I risultati ottenuti confermano una consistente riduzione del numero di falsi positivi, senza pagare in termini di precisione o rinunciare alla possibilità di eseguire il sistema su piattaforme embedded. [a cura dell'autore]XXIX n.s