Abstract
Analyzing video for traffic categorization is an important pillar of
Intelligent Transport Systems. However, it is difficult to analyze and
predict traffic based on image frames because the representation of each
frame may vary significantly within a short time period. This also would
inaccurately represent the traffic over a longer period of time such as
the case of video. We propose a novel bio-inspired methodology that
integrates analysis of the previous image frames of the video to
represent the analysis of the current image frame, the same way a human
being analyzes the current situation based on past experience. In our
proposed methodology, called IRON-MAN (Integrated Rational prediction
and Motionless ANalysis), we utilize Bayesian update on top of the
individual image frame analysis in the videos and this has resulted in
highly accurate prediction of Temporal Motionless Analysis of the Videos
(TMAV) for most of the chosen test cases. The proposed approach could be
used for TMAV using Convolutional Neural Network (CNN) for applications
where the number of objects in an image is the deciding factor for
prediction and results also show that our proposed approach outperforms
the state-of-the-art for the chosen test case. We also introduce a new
metric named, Energy Consumption per Training Image (ECTI). Since,
different CNN based models have different training capability and
computing resource utilization, some of the models are more suitable for
embedded device implementation than the others, and ECTI metric is
useful to assess the suitability of using a CNN model in multi-processor
systems-on-chips (MPSoCs) with a focus on energy consumption and
reliability in terms of lifespan of the embedded device using these
MPSoCs.