Extensive exploration of comprehensive vehicle attributes using D-CNN with weighted multi-attribute strategy

As a classical machine learning method, multi-task learning (MTL) has been widely applied in computer vision technology. Due to deep convolutional neural network (D-CNN) having strong ability of feature representation, the combination of MTL and D-CNN has attracted much attention from researchers re...

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Bibliographic Details
Published in:IET intelligent transport systems 2018-04, Vol.12 (3), p.186-193
Main Authors: Yan, Zhuo, Feng, Youji, Cheng, Cheng, Fu, Jianting, Zhou, Xiangdong, Yuan, Jiahu
Format: Article
Language:English
Subjects:
CompCars vehicle dataset
comprehensive vehicle attributes
deep convolutional neural network
D‐CNN
feedforward neural nets
learning (artificial intelligence)
make recognition
manufacturer recognition
MTL methods
multitask learning
object recognition
Research Article
surveillance images
vehicle model recognition
weighted multiattribute strategy
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Summary:As a classical machine learning method, multi-task learning (MTL) has been widely applied in computer vision technology. Due to deep convolutional neural network (D-CNN) having strong ability of feature representation, the combination of MTL and D-CNN has attracted much attention from researchers recently. However, this kind of combination has rarely been explored in the field of vehicle analysis. The authors propose a D-CNN enhanced with weighted multi-attribute strategy for extensive exploration of comprehensive vehicle attributes over surveillance images. Specifically, regarding to recognising vehicle model and make/manufacturer, several related attributes as auxiliary tasks are incorporated in the training process of D-CNN structure. The proposed strategy focuses more on the main task compared with traditional MTL methods, which has assigned different weights for the main task and auxiliary tasks rather than treating all involved tasks equally. To the extent of their knowledge, this is the first report relating to the combination of D-CNN and weighted MTL for exploration of comprehensive vehicle attributes. The following experiments will show that the proposed approach outperforms the state-of-the-art method for the vehicle recognition and improves the accuracy rate by about 10% for the analysis of other vehicle attributes on the recently public CompCars dataset.
ISSN:1751-956X
1751-9578
1751-9578
DOI:10.1049/iet-its.2017.0066