Grabcut-based abandoned object detection

Kahlil Muchtar, Chih Yang Lin, Chia Hung Yeh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents a detection-based method to subtract abandoned object from a surveillance scene. Unlike tracking-based approaches that are commonly complicated and unreliable on a crowded scene, the proposed method employs background (BG) modelling and focus only on immobile objects. The main contribution of our work is to build abandoned object detection system which is robust and can resist interference (shadow, illumination changes and occlusion). In addition, we introduce the MRF model and shadow removal to our system. MRF is a promising way to model neighbours' information when labeling the pixel that is either set to background or abandoned object. It represents the correlation and dependency in a pixel and its neighbours. By incorporating the MRF model, as shown in the experimental part, our method can efficiently reduce the false alarm. To evaluate the system's robustness, several dataset including CAVIAR datasets and outdoor test cases are both tested in our experiments.

Original languageEnglish
Title of host publication2014 IEEE International Workshop on Multimedia Signal Processing, MMSP 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781479958962
DOIs
Publication statusPublished - 2014 Nov 14
Externally publishedYes
Event2014 16th IEEE International Workshop on Multimedia Signal Processing, MMSP 2014 - Jakarta, Indonesia
Duration: 2014 Sep 222014 Sep 24

Publication series

Name2014 IEEE International Workshop on Multimedia Signal Processing, MMSP 2014

Conference

Conference2014 16th IEEE International Workshop on Multimedia Signal Processing, MMSP 2014
Country/TerritoryIndonesia
CityJakarta
Period2014/09/222014/09/24

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Signal Processing

Fingerprint

Dive into the research topics of 'Grabcut-based abandoned object detection'. Together they form a unique fingerprint.

Cite this