Abstract
In today’s intelligent transportation systems, vision-based traffic tracking systems and automatic vehicle make and model recognition is of significant importance for surveillance. From the literature, vehicle make and model recognition has been implemented for four wheelers easily due to the presence of logo in the frontal and rear parts. But it is not feasible for two wheelers due to the insignificant differential features. Hence in this work, a robust two-wheeler recognition system has been developed with frontal, rear and side images of two wheelers which is implementable in edge devices. The contributions of the paper are (i) Development of a two-wheeler dataset with the frontal, rear and side images of two classes in Indian Scenario. (ii) Development of a two-wheeler recognition framework implementable in Raspberry pi-based devices. (iii) Evaluation of the framework in real time outdoor environment. From the experimental results it is observed that the proposed network classifies two wheelers in real time at 4s with 94% accuracy.
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