  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.
