Authors:
Nada Hammami
;
Ala Mhalla
and
Alexis Landrault
Affiliation:
Institut Pascal, Clermont Auvergne University and France
Keyword(s):
Domain Adaptation, Deep Learning, Pedestrian Detection, Tracking, Optimization, Embedded System.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
Abstract:
Nowadays, the analysis and the understanding of traffic scenes become a topic of great interest in several computer vision applications. Despite the presence of robust detection methods for multi-categories of objects, the performance of detectors will decrease when applied on a specific scene due to a number of constraints such as the different categories of objects, the recording time of the scene (rush hour, ordinary time), the type of traffic (simple, dense) and the type of transport infrastructure. In order to deal with this problematic, the main idea of the proposed work is to develop a domain adaptation technique to automatically adapt detectors based on deep convolutional neural network toward a specific scene and to calibrate the network parameters in order to deploy it on an embedded platform. Results are presented for the proposed adapted detector in term of global performance in mAP and execution time onto a NVIDIA Jetson TX2 board.