Paper Info
Title
Real-time abandoned and stolen object detection based on spatio-temporal features in crowded scenes
Abstract
Abandoned and stolen object detection is a challenging task due to occlusion, changes in lighting, large perspective distortion, and the similarity in appearance of different people. This paper presents real-time detection methods of abandoned and stolen objects in a complex video. The adaptive background modeling method is applied to stable tracking and the ghost image removing. To detect abandoned and stolen objects, the methods determine spatio-temporal relationship between moving people and suspicious drops. The space first detection method measures the distance between a moving object and a non-moving object in spatial change analysis. The time first detection method conducts temporal change analysis and then spatial change analysis. The potential abandoned object is classified as a definite abandoned or stolen object by two-level detection approach. The time-to-live timer is applied by adjusting several key parameters on each camera and environment. In experiments, we show the experimental results to evaluate our proposed methods using benchmark datasets.
Year
DOI
Venue
2016
10.1007/s11042-015-2625-2
Multimedia Tools Appl.
Keywords
Field
DocType
Abandoned object,Stolen object,Left object,Background subtraction,Video surveillance
Background subtraction,Computer vision,Perspective distortion,Object detection,Pattern recognition,Computer science,Artificial intelligence,Timer,Change analysis,Temporal change
Journal
Volume
Issue
ISSN
75
12
1380-7501
Citations 
PageRank 
References 
2
0.36
21
Authors
1
Name
Order
Citations
PageRank
Yunyoung Nam126639.60