Motorcycle detection and classification in urban Scenarios using a model based on Faster R-CNN
Motorcycle detection and classification in urban Scenarios using a model based on Faster R-CNN
- Author(s): J.E. Espinosa ; S.A. Velastin ; J.W. Branch
- DOI: 10.1049/cp.2018.1292
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- Author(s): J.E. Espinosa ; S.A. Velastin ; J.W. Branch Source: 9th International Conference on Pattern Recognition Systems (ICPRS 2018), 2018 page (6 pp.)
- Conference: 9th International Conference on Pattern Recognition Systems (ICPRS 2018)
- DOI: 10.1049/cp.2018.1292
- ISBN: 978-1-78561-887-1
- Location: Valparaíso, Chile
- Conference date: 22-24 May 2018
- Format: PDF
This paper introduces a Deep Learning Convolutional Neutral Network model based on Faster-RCNN for motorcycle detection and classification on urban environments. The model is evaluated in occluded scenarios where more than 60% of the vehicles present a degree of occlusion. For training and evaluation, we introduce a new dataset of 7500 annotated images, captured under real traffic scenes, using a drone mounted camera. Several tests were carried out to design the network, achieving promising results of 75% in average precision (AP), even with the high number of occluded motorbikes, the low angle of capture and the moving camera. The model is also evaluated on low occlusions datasets, reaching results of up to 92% in AP.
Inspec keywords: image annotation; traffic engineering computing; motorcycles; image capture; feedforward neural nets; object detection; convolution; recurrent neural nets; cameras; image classification
Subjects: Traffic engineering computing; Computer vision and image processing techniques; Image recognition; Neural computing techniques
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