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A Viewpoint Aware Multi-Task Learning Framework for Fine-Grained Vehicle Recognition
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DOI：10.1109/ACCESS.2020.3024658.png)
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摘要
En 中文
The goal of fine-grained vehicle recognition is to identify the exact subtype of the vehicle from a given image. It plays an important role in the intelligent traffic surveillance system. Although fine-grained vehicle recognition has attracted more and more research interest, it remains as an open problem for vehicle images taken from arbitrary viewpoints. In this study, we present a one-stage deep multi-task learning framework for fine-grained vehicle recognition in traffic surveillance, which performs the fine-grained vehicle recognition and viewpoint estimation simultaneously. In the proposed framework, the fine-grained vehicle recognition is the main task which classifies images into different types, and the viewpoint estimation task is the auxiliary task to learn helpful viewpoint-aware features for improving the main task. We evaluate our method on two common used large-scale fine-grained vehicle recognition datasets, including BoxCars116k and CompCars. The experimental results testify that the proposed multi-task framework can improve the accuracy by incorporating the viewpoint information of the vehicles. In comparison to the state-of-the-art, our approach goes beyond, only requiring 3D bounding box for the training phase, which is important for future inferences using the trained model.
Keyword:
Task analysis
Estimation
Three-dimensional displays
Solid modeling
Surveillance
Feature extraction
Image recognition
Fine-grained recognition
vehicle type verification
multi-task learning
geometry estimation
viewpoint estimation
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期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
An Ensemble Deep Learning Method for Vehicle Type Classification on Visual Traffic Surveillance Sensors.png)
IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
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