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Piuri Vincenzo - Semantic Analysis and Understanding of Human Behavior in Video Streaming

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Book:
Semantic Analysis and Understanding of Human Behavior in Video Streaming
Author:
Piuri Vincenzo / Di Lecce Vincenzo / Amato Alberto
Publisher:
Springer
Genre:
Books / Computer
Year:
2013
City:
New York;NY
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Alberto Amato , Vincenzo Di Lecce and Vincenzo Piuri Semantic Analysis and Understanding of Human Behavior in Video Streaming 2013 10.1007/978-1-4614-5486-1_1 Springer Science+Business Media New York 2013

1. Introduction

Alberto Amato 1

(1)

Politecnico di Bari, Viale de Turismo, 74123 Taranto, Italy

(2)

Universit degli Studi di Milano, Via Bramante, 26013 Crema, Italy

Alberto Amato

Email:

Vincenzo Di Lecce

Email:

Vincenzo Piuri (Corresponding author)

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Abstract

This book proposes a new methodology to automatically analyze human behavior in narrow domains using video streaming. This chapter begins discussing the main technological innovations that are creating the premise to implement such kind of systems. Next, the principal application fields of these systems are briefly presented and then the main issues that the scientific community are facing to realize them are presented. Finally the proposed methodology is briefly presented and the remaining part of the book is outlined.

1.1 Introduction

Aim of this book is to investigate, both from a theoretical and a technological point of view, the problem of human behavior analysis in video streaming. From a psychological point of view, the general concept of behavior is the reaction of human beings to a set of external and internal impulses. These impulses may be caused by very different sources, and only the combination of them can represent an objective explanation of the observed behavior.

In the latest years, information and communication technology (ICT) has had a strong improvement having significant influence on our everyday life. The effects of the technological improvements in the fields of sensor manufacturing and communication networks are particularly relevant. For what concerns the latter, nowadays is the Internet Age. Millions of computers are connected among them, sharing data and hosting services for a large number of users living everywhere in the world. Furthermore, the network is becoming pervasive. Indeed, thanks to the spread of wireless networks it is possible to be connected everywhere also by means of devices which are different from the traditional PC (notebook, netbook, smartphone, etc.).

For what concerns sensors, in these days there is a fast evolution toward smaller sensors and with increasing performance, e.g., in terms of precision, accuracy, and reliability. This has led to the development of a large number of applications. For example, nowadays the major part of videos and pictures are taken by using digital devices, while sensors and actuators based on the Micro Electro-Mechanical Systems (MEMS) technology are the key elements of many applications for the modern smartphones.

These factors have also enabled and booted the development of sensor networks that are able to monitor wide areas where human beings perform their activities. Studying data recorded by these sensor networks we can develop technologies and systems for high semantic level analysis of the human behavior [1]. The aim of these technologies consists of defining a description of the human behavior that can be used in recognition tasks. Human behavior analysis can be coupled with biometric systems [27] in order to recognize also the identity of the person performing a given action. On the other hand it should be noticed that this kind of system could have a strong influence on the privacy. For the interested reader, an in depth analysis of this aspect can be found in [812].

The analysis from a psychological point of view of the reasons that have determined a given behavior is beyond the scope of these technologies.

In the latest years, automatic human behavior analysis has attracted the interest of the international scientific community. This strong interest is due to the many potential applications that such kind of systems can have. Essentially, these applications can be divided into three macro areas:

Surveillance . This application area has a relevant interest also due to the facts characterizing the history of the last decade. In particular, the aim of the applications in this area is monitoring and understanding human behavior in public and crowded areas (such as streets, bus and train stations, airports, shopping malls, sport arenas, and museums). These applications can be used both for management (for example: evaluation of crowding in the monitored areas, human flow analysis, and detection of the congestion points) and security (for example: human behavior analysis, activity recognition of individuals and groups, and detection of suspicious persons) [13].
Control . In this area the researchers try to develop systems that are able to evaluate some human motion parameters (i.e. speed, gait) and/or the poses (i.e. the mutual position of the harms, or the position of the head) to control actions and/or operations [14]. One of the most important fields of application for these systems is the humanmachine interaction. On the market there are some interesting uses as input devices for videogames. These devices use both video and inertial sensors to evaluate the players motion and understand his/her commands.
Analysis . Applications of human motion analysis are used in various fields, including: sports (as an aid for evaluating the techniques and the performance of the athletes), and medicine (as an aid in the diagnosis of problems in human postures and in the orthopedic rehabilitation) [15].

The economic and social relevance of potential applications (especially the security, entertainment, and medical ones), the scientific complexity, the speed and price of current hardware intensified the effort within the scientific community towards automatic capture and analysis of human motion.

In the literature, various approaches have been proposed to capture the human behavior. Most of them use video sensors since these technologies are not invasive and rather cheap, as well as they produce good-quality data suited for being processed by means of inference techniques mimicking the human ones. Some authors proposed alternative approaches by using other kind of sensors, such as inertial sensors, audio sensors, presence detectors; unfortunately, these sensors (often installed in devices as wearable sensors) are characterized by some invasiveness and, therefore, can be used only in some specific control applications [16, 17]. Consequently, for these reasons, this book addresses the human behavior analysis by using video streaming as the most appropriate technology to observe the human behavior.

Automatic understanding of the human behavior from video sequences is a very challenging problem since it implies understanding, identifying, and either mimicking the neuro-physiological and psychological processes, which are naturally performed in humans or creating similar outcomes by means of appropriate information and knowledge processing. In order to achieve this goal the problem has been split into two steps:

A compact representation of the real world is first defined by using the data sampled by cameras. This representation should be as close as possible to the reality, view invariant, and reliable for subsequent processing. A video streaming contains a large amount of data, but often they are redundant and/or useless for the human behavior analysis (for example: the static data about the background scene is not helpful). Therefore, it is necessary to track the areas where a difference between two successive video frames has been detected in order to focus the attention on the areas in which there are moving objects (may be humans), while discarding the background with irrelevant information. Later these moving entities will be identified as human beings or unanimated objects (or animals). Moving entities will be traced through the various frames to characterize their movements.

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