Deep learning-based multiple moving vehicle detection and tracking using a nonlinear fourth-order reaction-diffusion based multi-scale video object analysis

Abstract

<p style='text-indent:20px;'>A novel automatic vehicle detection and tracking framework is proposed in this research work. First, the moving vehicles are detected in the frames of the video sequence by combining some deep learning and Gaussian Mixture Model-based object detection techniques. Then, the correspondence between the video objects detected in successive frames is determined using a multi-scale analysis of those objects. A scale-space representation is created by applying the numerical approximation algorithm that solves a nonlinear fourth-order reaction-diffusion based model whose mathematical validity is rigorously investigated here. A color image feature extraction is then performed at each scale using SURF and HOG-based features and the feature vectors determined at multiple scales are next concatenated into a final descriptor. A novel instance matching-based vehicle tracking technique using the distances between these feature vectors is then proposed. The results of the performed detection and tracking simulations are finally discussed.</p>