Design and Implementation of a Counting and Differentiation System for Vehicles through Video Processing

This paper presents a self-sustaining mobile system for
counting and classification of vehicles through processing video. It
proposes a counting and classification algorithm divided in four steps
that can be executed multiple times in parallel in a SBC (Single
Board Computer), like the Raspberry Pi 2, in such a way that it
can be implemented in real time. The first step of the proposed
algorithm limits the zone of the image that it will be processed.
The second step performs the detection of the mobile objects using
a BGS (Background Subtraction) algorithm based on the GMM
(Gaussian Mixture Model), as well as a shadow removal algorithm
using physical-based features, followed by morphological operations.
In the first step the vehicle detection will be performed by using
edge detection algorithms and the vehicle following through Kalman
filters. The last step of the proposed algorithm registers the vehicle
passing and performs their classification according to their areas.
An auto-sustainable system is proposed, powered by batteries and
photovoltaic solar panels, and the data transmission is done through
GPRS (General Packet Radio Service)eliminating the need of using
external cable, which will facilitate it deployment and translation to
any location where it could operate. The self-sustaining trailer will
allow the counting and classification of vehicles in specific zones
with difficult access.

Authors:

• Derlis Gregor
• Kevin Cikel
• Mario Arzamendia
• Raúl Gregor

Keywords:

• Intelligent transportation systems
• object detection
• video processing
• road traffic
• vehicle counting
• vehicle classification.

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