Paper Info
Title
MAT-CNN-SOPC: Motionless Analysis of Traffic Using Convolutional Neural Networks on System-On-a-Programmable-Chip.
Abstract
Intelligent Transportation Systems (ITS) have become an important pillar in modern "smart city" framework which demands intelligent involvement of machines. Traffic load recognition can be categorized as an important and challenging issue for such systems. Recently, Convolutional Neural Network (CNN) models have drawn considerable amount of interest in many areas such as weather classification, human rights violation detection through images, due to its accurate prediction capabilities. This work tackles real-life traffic load recognition problem on System-On-a-Programmable-Chip (SOPC) platform and coin it as MAT-CNN-SOPC, which uses an intelligent retraining mechanism of the CNN with known environments. The proposed methodology is capable of enhancing the efficacy of the approach by 2.44x in comparison to the state-of-art and proven through experimental analysis. We have also introduced a mathematical equation, which is capable of quantifying the suitability of using different CNN models over the other for a particular application based implementation.
Year
DOI
Venue
2018
10.1109/ahs.2018.8541406
NASA/ESA Conference on Adaptive Hardware and Systems
Keywords
DocType
Volume
Convolutional neural network (CNN),traffic analysis,traffic density,transfer learning,system-on-a-programmable-chip (SOPC)
Conference
abs/1807.02098
ISSN
Citations 
PageRank 
1939-7003
1
0.35
References 
Authors
0
6
Name
Order
Citations
PageRank
Somdip Dey1105.29
Grigorios Kalliatakis2124.95
Sangeet Saha3175.75
Amit Kumar Singh410.35
Shoaib Ehsan511024.43
Klaus D. McDonald-Maier632754.43