Name
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/MMSP.2014.6958806 | Multimedia Signal Processing |
Keywords | Field | DocType |
Markov processes,image motion analysis,image representation,object detection,security,video surveillance,CAVIAR dataset,MRF model,Markov random field,abandoned object subtraction,background modelling,background object,crowded scene,detection-based method,grabcut-based abandoned object detection,illumination change,immobile object,interference resistance,neighbour information modeling,occlusion resistance,pixel correlation representation,pixel dependency representation,pixel labeling,robustness,shadow removal,surveillance scene | Shadow,Object detection,Computer vision,Viola–Jones object detection framework,False alarm,Pattern recognition,Computer science,GrabCut,Robustness (computer science),Artificial intelligence,Test case,Pixel | Conference |
ISSN | Citations | PageRank |
2163-3517 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kahlil Muchtar | 1 | 27 | 6.39 |
| Chih-Yang Lin | 2 | 393 | 48.15 |
| Chia-Hung Yeh | 3 | 367 | 42.15 |