What is it about?
In this paper, we propose the principal component analysis network-based convolutional neural network (PCNN) and pinpoint only one discriminative local feature of a vehicle, which is the vehicle headlamp, for vehicle model recognition. The proposed model eliminates the need for locating and segmenting the headlamp precisely. In particular, PCNN ascertains the effectiveness of both principal component analysis and CNN in extracting hierarchical features from a vehicle headlamp image and also reducing the computational complexity of the traditional CNN system.
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Photo by Anton Ivanchenko on Unsplash
Why is it important?
Experiment results show that PCNN outperforms state-of-the-art techniques with an average accuracy of 99.51% over 38 vehicle makes and models using the PLUS data set. In addition, the effectiveness of the proposed method is also validated using the public CompCars data set, achieving 89.83% accuracy over 357 vehicle models.
Perspectives
A highly accurate AI architecture for vehiclce model recognition.
Dr Joon Huang Chuah
University of Malaya
Read the Original
This page is a summary of: PCANet-Based Convolutional Neural Network Architecture For a Vehicle Model Recognition System, IEEE Transactions on Intelligent Transportation Systems, January 2018, Institute of Electrical & Electronics Engineers (IEEE),
DOI: 10.1109/tits.2018.2833620.
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