Intelligent Surveillance Systems: Mobile Object Detection, Tracking and Pattern Analysis

1. Introduction

Surveillance systems are used to monitor and record the activities of people in public places for security purposes. The main purpose to have a surveillance system is to understand and predict the ramifications and interactions in any one given scene of objects under observation. To achieve this, different methods have been proposed in the literature for automatically detecting, recognising and tracking people and vehicles in video sequences captured by stationary or mobile cameras , , , . A significant body of research has also been devoted to the use of artificial intelligence (Al) techniques for the analysis of video data from surveillance cameras , , . Intelligent surveillance, as defined in , is the process of providing situational awareness and decision support by fusing together information from multiple sources, including video, through the use of Al techniques. This paper provides an overview of some of the key issues related to intelligent surveillance systems, with a focus on mobile object detection, tracking and pattern analysis.

2. Mobile object detection

A mobile object can be any type of moving entity that can be detected by a camera, such as a person, vehicle or animal. The first step in any intelligent surveillance system is to detect these objects in the scene so that they can be tracked over time. There are many challenges associated with detecting mobile objects, particularly when they are small or partially obscured from view. Different approaches have been proposed in the literature for solving this problem, including background subtraction, frame differencing, optical flow estimation and template matching .

3. Tracking of mobile objects

Once mobile objects have been detected in the scene, they need to be tracked so that their motion can be analysed over time. This is usually achieved by associating each object with a tracklet, which is a sequence of frames in which the object appears. There are many challenges associated with tracking mobile objects, such as occlusions, changes in appearance and sensor noise. Different approaches have been proposed for solving this problem, including Kalman filters, particle filters and Markov chain Monte Carlo methods .

4. Pattern analysis for intelligent surveillance

One of the main goals of intelligent surveillance is to detect unusual or unexpected behaviour in the scene so that appropriate action can be taken. This requires the analysis of patterns of behaviour over time, which can be achieved using a variety of methods such as rule-based reasoning, decision trees and artificial neural networks .

5. Artificial intelligence for intelligent surveillance

Artificial intelligence (AI) techniques have been extensively used for various tasks in intelligent surveillance systems, such as object detection, tracking and behaviour analysis. Some popular AI techniques include rule-based reasoning, decision trees, artificial neural networks and support vector machines .

6. Conclusions

In this paper we have provided an overview of some key issues related to intelligent surveillance systems. We have described how mobile objects can be detected and tracked using different methods, and how pattern analysis can be used to detect unusual behaviour in the scene. We have also discussed how AI techniques can be used for various tasks in intelligent surveillance systems.

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