Fuzzy logic based human activity recognition in video ...

Fuzzy logic based human activity recognition in video surveillance applications Slim ABDELHEDI1 , Ali WALI2 , and Adel M. ALIMI3 REGIM: REsearch Groups in Intelligent Machines, University of Sfax, National Engineering School of Sfax (ENIS), BP 1173, Sfax, 3038, Tunisia {slim.abdelhedi.tn1 , ali.wali2 , adel.alimi3 }@ieee.org

Abstract. Automatic fall detection using computer vision is a particular case for real time video analysis, efficient in kindergartens. This paper is focused on the design and implementation of a Human activity analysis system. The multiple cameras sends captured frames to the monitoring system via the local network. Through the use of human silhouette, acquired from a smart camera, a shape representation of the human beings was built in real-time and a fuzzy logic inference system was developed for fall detection. The system also allows tracking and localizing children within an authorized area. The alarm is triggered in case of transgression. Experimental results prove that the fuzzy inference system is efficient.

1

Introduction

Children in kindergartens are prone to the risk of falls and the outcoming injuries might be serious. Therefore, it is essential to monitor their movement in a real time in order to prevent injuries and intervene to provide the necessary help as soon as possible. In this work, we introduce an intelligent fall detection system based on video surveillance and background subtraction technique. The proposed system allows a good detection, activity analysis and monitoring of children in a kindergarten, and triggers alarms when a child leaves the permitted zone whether indoors or outdoors. Instead of using a physical fence, the system uses virtual fences positioned within each camera image. For the surveillance of a large area, one or more cameras are installed and a kindergarten video dataset [1] is used. Our work deals with the design and implementation of an intelligent security system using the multi-camera and OpenCV library1 . This approach allows to prevent children falls and their possible adverse results. In fact, it allows taking every precaution to inform the staff of the real-time children status. A video surveillance cameras were placed in different zones so as to cover the whole institution venue and video sequences were continuously logged. The falls and non-falls were respectively detected using automated algorithms to process camera board video frame in real kindergarten environments. 1

http://sourceforge.net/projects/opencvlibrary/

2

The system has the capability of detecting the motion of a human and their localization by the background subtraction algorithm [1]. When the moving human is detected, the system can classify them as a fall or no-fall [2]. If there is fall detection, the system sends out an alerting signal to the staff of a kindergartens via the Local Area Network (LAN). Many studies have been done on human activities recognition , especially on fall detections based on computer Vision. Foroughi et al. [2] developed an approach for fall detection using a combination of the Eigen Space method an integrated time motion images. In [3] an approximated ellipse was used around the human body for shape change. In [4], an Omni-Camera called MapCam was employed to capture images and detect falls using the Background Subtraction method. Miaou et al. [5] developed a fall detection system based on foreground extraction and height over width parameters. This paper is arranged in 2 sections. Section 2 provides a proposed system architecture using multi-camera. Section 3 presents the image processing techniques and explains the basics of the fuzzy inference engine used to classify human action and the obtained experimental results.

2

Proposed system for kindergarten video surveillance

For kindergarten safety, the area is covered by multiple cameras. Our system is capable of tracking persons and determining their location and activity. It detects breaches to secure the zones, unauthorized activity, falling in specific areas and perimeter intrusion then generates an alarm. All videos frames or control signals are transferred over the network in real time for control and visualization. The human detection system is based on a IP cameras attached to the switch port at FastEthernet speeds and monitoring system [6] . In a LAN network, the IP camera is attached to the switch port and records video sequences continuously and detects human motion in the kindergarten rooms. When the security system detects an abnormal behaviour, the monitoring system sends an alarm indicating the breach location. The Emergency Alert Notification System (EANS) is used to alert staff by sending warnings via text messages and e-mail messages including the video and image of the children in case of emergency. The system allows to access all information of children in kindergartens from anywhere at anytime using the web server, and perform real-time acquisition.

3

Image processing

Many techniques are used to extract the background model depending on the video scene. Extracting moving objects is difficult due to many factors, such as motion changes and frame moving. The fall detection system process can be described in three steps: first the background is modeled by the Type-2 Fuzzy Gaussian Mixture Models (T2 FGMMs) algorithm presented by Abdelhedi et al. [1] for each input frame, then the object is segmented and extracted, and finally