MAT-CNN-SOPC: Motionless Analysis of Traffic Using Convolutional Neural Networks on System-On-a-Programmable-Chip

TL;DR

This paper presents MAT-CNN-SOPC, a novel system for traffic load recognition on SOPC platforms using CNNs with an intelligent re-training mechanism, significantly improving accuracy and providing a mathematical model for CNN suitability.

Contribution

It introduces a re-training CNN approach on SOPC for traffic analysis and a mathematical equation to evaluate CNN model suitability for specific applications.

Findings

Enhanced efficacy by 2.44x over state-of-the-art methods

Proposed mathematical model for CNN suitability assessment

Validated through experimental analysis

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 re-training 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.