Paper Info
Title
Moving object detection and classification using neural network
Abstract
Moving object detection and classification is an essential and emerging research issue in video surveillance, mobile robot navigation and intelligent home networking using distributed agents. In this paper, we present a new approach for automatic detection and classification of moving objects in a video sequence. Detection of moving edges does not require background; only three most recent consecutive frames are utilized. We employ a novel edge segment based approach along with an efficient edge-matching algorithm based on integer distance transformation, which is efficient considering both accuracy and time together. Being independent of background, the proposed method is faster and adaptive to the change of environment. Detected moving edges are utilized to classify moving object by using neural network. Experimental results, presented in this paper demonstrate the robustness of proposed method.
Year
DOI
Venue
2008
10.1007/978-3-540-78582-8_16
KES-AMSTA
Keywords
Field
DocType
mobile robot navigation,distance transform,neural network
Object detection,Computer vision,Viola–Jones object detection framework,Motion detection,Object-class detection,Computer science,Robustness (computer science),Video tracking,Artificial intelligence,Mobile robot navigation,Artificial neural network,Machine learning
Conference
Volume
ISSN
ISBN
4953
0302-9743
3-540-78581-7
Citations 
PageRank 
References 
3
0.37
11
Authors
3
Name
Order
Citations
PageRank
M. Ali Akber Dewan1799.53
M. Julius Hossain2739.50
Oksam Chae361646.52