An Abandoned Object Detection System Based on Dual Background Segmentation

Abstract

Abstract-An abandoned object detection system is presented and evaluated using benchmark datasets. The detection is based on a simple mathematical model and works efficiently at QVGA resolution at which most CCTV cameras operate. The pre-processing involves a dual-time background subtraction algorithm which dynamically updates two sets of background, one after a very short interval (less than half a second) and the other after a relatively longer duration. The framework of the proposed algorithm is based on the Approximate Median model. An algorithm for tracking of abandoned objects even under occlusion is also proposed. Results show that the system is robust to variations in lighting conditions and the number of people in the scene. In addition, the system is simple and computationally less intensive as it avoids the use of expensive filters while achieving better detection results.

Abandoned Object Detection Using Dual Background Model from Surveillance Videos

“…Madasu and B.C. Lovell [12] have presented their work on an abandoned object detection system based on dual background segmentation. In this paper, the system is based on a simplistic and intuitive mathematical model.…”

Abandoned Object Detection and Tracking in Video Surveillance

“…Their back-tracking algorithm iteratively tracks the luggage owner by using spatialtemporal windows to efficiently verify left-luggage events. A.Singh et al [2] proposed an object detection system based on a dual background segmentation scheme. The background segmentation is adaptive in the nature and based on the Approximate Median Model.…”

Enhancement of Background Subtraction Techniques Using a Second Derivative in Gradient Direction Filter

“…The approximate median (AM) algorithm is adaptive, dynamic, nonprobabilistic, and intuitive [8]. AM is obtained by calculating the difference between two video frames and using this difference in determining the perfect method for updating the background.…”

“…The technique involves comparing or subtracting the current frames from the background frame and treating the remaining pixels as foreground [1]. Prior research on background subtraction (BGS) used several parametric BGS techniques, such as running average [2][3][4], running Gaussian average [5][6][7], approximate median filter [7,8], and Gaussian Mixture Model [9][10][11]. These parametric techniques determine the foreground and update the subsequent background based on the distribution of intensity value [12].…”