Centre of Excellence in Machine Learning

Project 1: Car model analysis with artificial neural networks

Objectives

• Explore possible neural architectures to handle the problem of car model identification using grained features.
• Reproduce the results reported in the paper linked here.

Practical applications

• Paying tolls
• Video surveillance and car verification
• Smart cities
• Mobile applications to instantly show car information
• Predicting popularity of vehicles

Datasets

• The Comprehensive Cars (CompCars) dataset
• Stanford dataset

Tools/Frameworks

• Caffe.
• Keras with TensorFlow backend.

Expected Results

• Deliver a trained neural network for car model identification
• Outperform accuracy scores reported by the chinese paper.

Checklist

• Request CompCars database.
• Define development machine and database storage.
• Explore the data and gain insights.
• Evaluation and define preprocessing steps.
• Implementation of the neural network (NN) models.
  • Deep convolutional neural network: Inception.
  • Pulsed-coupled NN.
• Presentation of results in any relevant conference (to define...)

References

• A Large-Scale Car Dataset for Fine-Grained Categorization and Verification.
• Three-Dimensional Deformable-Model-Based Localization and Recognition of Road Vehicles.

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