Vehicle Re-Identification in Multi-Camera scenarios based on Ensembling Deep Learning Features
CVPR Workshops(2020)
摘要
Vehicle re-identification (ReID) across multiple cameras is one of the principal issues in Intelligent Transportation System (ITS). The main challenge that vehicle ReID presents is the large intra-class and small inter-class variability of vehicles appearance, followed by illumination changes, different viewpoints and scales, lack of labelled data and camera resolution. To address these problems, we present a vehicle ReID system that combines different ReID models, including appearance and orientation deep learning features. Additionally, for results refinement re-ranking and a post-processing step taking into account the vehicle trajectory information provided by the CityFlow-ReID dataset are applied.
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关键词
vehicle re-identification,multicamera scenarios,ensembling deep learning features,multiple cameras,Intelligent Transportation System,intra-class variability,inter-class variability,vehicle appearance,camera resolution,vehicle ReID system,ReID models,orientation deep learning features,vehicle trajectory information,CityFlow-ReID dataset,ITS,refinement re-ranking,post-processing step,Re
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