A Fuzzy rule- based Abandoned Object Detection using Image Fusion for Intelligent Video Surveillance Systems

Preetha K G Et.Al

doi:10.17762/turcomat.v12i3.1652

Abstract

Abandoned object/luggage is a major threat in all public scenes like hospitals, railway stations, airports and shopping malls. Abandoned luggage may contain explosive, biological warfare or smuggled goods. Abandoned object detection is the process to identify the unattended strange object within a specific time. It is also crucial to identify the person who has abandoned the luggage in the scene. Video surveillance is one of the essential techniques for automatic video analysis to extract crucial information or relevant scenes. The main objectives of this work is the automatic detection of abandoned objects and related persons in public areas like airports, railway stations, shopping malls etc. Video enhancement techniques like residual dense networks are adopted to improve the quality of the image before applying it to detect the abandoned objects and related humans. The scenario of abandoned objects and related humans are identified through distance differencing methods. Once the scene is identified, the method is capable of producing alert messages or alarms in real-time through automated means. A fuzzy rule based threat assessment module is also incorporated in this work which reduces the false alarm rate. The related person is identified through reconstruction of the face through super-resolution techniques. Experiments are found to be appreciable in terms of the metrics in video enhancement, detection, fuzzification and face super-resolution.

Highlights

In the context of different types of attacks happening all around the world, it is seen that the lives of innocent people are affected
In order to understand the merits of residual dense networks, it is compared with the traditional methods of enhancement like Histogram Equalization (HE) and Contrast Limited Adaptive Histogram Equalization (CLAHE)
The metrics used for evaluation are Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Mean Squared Error (MSE)

Summary

Introduction

In the context of different types of attacks happening all around the world, it is seen that the lives of innocent people are affected Considering this scenario, it is highly necessary to have an automated surveillance system that alerts real-time potential threats in the environment. Abandoned object/luggage is seen as a major threat in all public scenes like hospitals, railway stations, airports and shopping malls, as these are usually carriers of explosives, biological warfare and even smuggled goods. For this reason, it is highly necessary to detect the abandoned objects as well as the human beings who are the cause of this action. The accuracy of most of the computer vision, image processing and machine learning tasks which is used to perform video analysis can be improved by enhancing the image appearance and quality

Objectives

Results

Conclusion