Paper Info
Title
Robust Real Time Moving People Detection in Surveillance Scenarios
Abstract
In this paper an improved real time algorithm for detectingpedestrians in surveillance video is proposed. Thealgorithm is based on people appearance and defines a personmodel as the union of four models of body parts. Firstly,motion segmentation is performed to detect moving pixels.Then, moving regions are extracted and tracked. Finally,the detected moving objects are classified as human or nonhumanobjects. In order to test and validate the algorithm,we have developed a dataset containing annotated surveillancesequences of different complexity levels focused onthe pedestrians detection. Experimental results over thisdataset show that our approach performs considerably wellat real time and even better than other real and non-realtime approaches from the state of art.
Year
DOI
Venue
2010
10.1109/AVSS.2010.33
AVSS
Keywords
Field
DocType
wellat real time,annotated surveillancesequences,onthe pedestrians detection,improved real time algorithm,robust real time,people appearance,body part,surveillance scenarios,non-realtime approach,people detection,motion segmentation,different complexity level,detectors,real time,pixel,tracking,image segmentation,real time systems
Object detection,Computer vision,Pattern recognition,Segmentation,Computer science,Image segmentation,Artificial intelligence,Pixel,Detector
Conference
Citations 
PageRank 
References 
12
0.94
28
Authors
2
Name
Order
Citations
PageRank
Memarmoghadam Alireza11777.02
José M. Martínez2598.64