Sensor Selection and Integration to Improve Video Segmentation in Complex Environments

Adam R Reckley,E-Wen Huang,Chung-Hao Chen,Wei-Wen Hsu,Gangfeng Ma

doi:10.1155/2014/654790

Abstract

Background subtraction is often considered to be a required stage of any video surveillance system being used to detect objects in a single frame and/or track objects across multiple frames in a video sequence. Most current state-of-the-art techniques for object detection and tracking utilize some form of background subtraction that involves developing a model of the background at a pixel, region, or frame level and designating any elements that deviate from the background model as foreground. However, most existing approaches are capable of segmenting a number of distinct components but unable to distinguish between the desired object of interest and complex, dynamic background such as moving water and high reflections. In this paper, we propose a technique to integrate spatiotemporal signatures of an object of interest from different sensing modalities into a video segmentation method in order to improve object detection and tracking in dynamic, complex scenes. Our proposed algorithm utilizes the dynamic interaction information between the object of interest and background to differentiate between mistakenly segmented components and the desired component. Experimental results on two complex data sets demonstrate that our proposed technique significantly improves the accuracy and utility of state-of-the-art video segmentation technique.

Highlights

Background subtraction is often considered to be a key part of any video surveillance system being used to detect objects in a single frame and/or track objects across multiple frames in a video sequence
We hypothesize that the dynamic interaction between an object of interest and the background can provide useful information and by understanding and modeling the dynamic interaction between an object of interest and background, we can improve the performance of state-of-the-art object detection techniques
Based upon the fact that, of the three trials that were performed, trial 1 had the smallest movement per image, we believe that the fuzzy rule-based method failed on this image sequence due to the limited movement of the desired object of interest that caused the desired object of interest to be incorporated into the background of the image

Summary

Introduction

Background subtraction is often considered to be a key part of any video surveillance system being used to detect objects in a single frame and/or track objects across multiple frames in a video sequence. There are many proposed methods for object detection, a number of them achieving moderate success in dynamic backgrounds, most current state-of-the-art techniques treat objects of interest and background as separate entities ignoring any interaction. The motion of an object in the ocean is going to be strongly influenced by wave action. Another example, if the object of interest is a specific vehicle, any vehicles in front of the object of interestthat slow or stop will cause the object of interest to begin to slow or slow to a stop. We hypothesize that the dynamic interaction between an object of interest and the background can provide useful information and by understanding and modeling the dynamic interaction between an object of interest and background, we can improve the performance of state-of-the-art object detection techniques. We select the best quality of information in terms of relevant parameters and dynamically assessing these parameters in a multisensor setting to be integrated image segmentation process

Objectives

Results

Conclusion