Abstract- Intelligent vision systems (IVS) represent an exciting part of modern sensing, computing, and engineering systems. The principal information source in IVS is the image, a two dimensional representation of a three dimensional scene. The main advantage of using IVS systems is that the information is in a form that can be interpreted by humans.

Our paper is an image process application for abnormal incident detection, which can be used in high security installation, subways, etc. In our work, motion cues are used to classify dynamic scenes and subsequently allow the detection of abnormal movements, which may be related critical situations.

Successive frames are extracted from the video stream and compared. By subtracting the second image from the first, that difference image is obtained. This is the segmented to aid error measurement and thresholding. If is the threshold is exceeded, the human operator is alerted. So, that he / she may take remedial action. Thus by processing the input image suitably, our system alerts operators to any abnormal incidents, which might lead to critical situations.

1. Introduction

1.1. Need for automated Surveillance

Motion-based automated surveillance or intelligent scene-monitoring systems were introduced in the recent past. Video motion detection and other similar systems aim to alert operators or start a high-resolution video recording when the motion conditions of a specific area in the scene are changed.

In recent years interest in automated surveillance systems has grown dramatically as the advances in image processing and computer hardware technologies have made it possible to design intelligent incident detection algorithms and implemented them as real-time systems. The need for such equipment has been obvious for quite some time now, as human operators are unreliable, fallible and expensive to employ.

1.2. Motion analysis for incident detection

Interest in motion processing has increased with advance in motion analysis methodology and processing capabilities. The concept of automated incident detection is based on the idea of finding suitable image cues that can represent the specific event of interest with minimum overlapping with other classes. In this paper, motion is adopted as the main cue for abnormal incident detection.

1.3. Image acquisition

Obtaining the images is the first step in implementing the system.

1.4. Camera position

The camera is placed at a fixed height in the subway or corridor. This portion need not be changed along the course of operation.

1.5. Frame extraction

In this system, motion is used as the main cue for abnormal incident detection. It is henceforth obvious that the first concern is obtaining the images required from the source. In the circumstances described (subways, high security installations) usually a closed circuit television system is employed.

Any ordinary video systems use 25 frames per second. The system described here uses scene motion information extracted at the rate of 8.33 times per second. These amounts to capturing a frame once every two frames in the video camera system. In practical real time operation a hardware block-matching motion detector is used for frame extraction.