Affiliations: [a] Department of Electrical and Computer Engineering, The University of Texas at San Antonio, San Antonio, TX, USA | [b] Department of Architecture, The University of Texas at San Antonio, TX, USA
Correspondence:
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Corresponding author: Yuvaraj Munian, Department of Electrical and Computer Engineering, The University of Texas at San Antonio (UTSA), San Antonio, TX 78249, USA. E-mail: [email protected].
Abstract: Animal Vehicle Collision (AVC) is relatively an evolving source of fatality resulting in the deficit of wildlife conservancy along with carnage. It’s a globally distressing and disturbing experience that causes monetary damage, injury, and human-animal mortality. Roadkill has always been atop the research domain and serendipitously provided heterogeneous solutions for collision mitigation and prevention. Despite the abundant solution availability, this research throws a new spotlight on wildlife-vehicle collision mitigation using highly efficient artificial intelligence during nighttime hours. This study focuses mainly on arousal mechanisms of the “Histogram of Oriented Gradients (HOG)” intelligent system with extracted thermography image features, which are then processed by a trained, convolutional neural network (1D-CNN). The above computer vision – deep learning-based alert system has an accuracy between 94%, and 96% on the arousal mechanisms with the empowered real-time data set utilization.
