Authors:
D. S. Guru
;
Elham Dallalzadeh
and
S. Manjunath
Affiliation:
University of Mysore, India
Keyword(s):
Corner-based tracking, Shape reconstruction, Shape normalization, Shape feature extraction, Interval-valued feature vector, Symbolic representation, Symbolic similarity measure, Vehicle classification.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Classification
;
Computational Learning Theory
;
Computer Vision, Visualization and Computer Graphics
;
Image and Video Analysis
;
Object Recognition
;
Pattern Recognition
;
Shape Representation
;
Software Engineering
;
Theory and Methods
;
Video Analysis
Abstract:
In this paper, a symbolic approach is proposed to classify moving vehicles in traffic videos. A corner-based tracking method is presented to track and detect moving vehicles. We propose to overlap the boundary curves of each vehicle while tracking it in sequence of frames to reconstruct a complete boundary shape of the vehicle. The reconstructed boundary shape is normalized and then a set of efficient shape features are extracted. The extracted shape features are used to form interval-valued feature vector representation of vehicles. Vehicles are categorized into 4 different types of vehicle classes using a symbolic similarity measure. To corroborate the efficacy of the proposed method, experiment is conducted on 21,239 frames of roadway traffic videos taken in an uncontrolled environment during day time. The proposed method has 95.16% classification accuracy. Moreover, experiments reveal that the proposed method can be well adopted for on-line classification of moving vehicles as it
is based on a simple matching scheme.
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