Shankaranarayan, N.Kamath S․, S.2026-02-062023Springer Proceedings in Mathematics and Statistics, 2023, Vol.401, , p. 203-21221941009https://doi.org/10.1007/978-3-031-15175-0_16https://idr.nitk.ac.in/handle/123456789/29507Automated smart traffic surveillance systems constitute a significant part of smart city environments and have attracted significant research attention in recent years. Vehicle re-identification is a major challenge in automated traffic surveillance systems in smart city environments. Vehicle re-identification is the process of retrieving instances of the target vehicle given a gallery of numerous vehicle images. Though multiple models were proposed to perform the task of vehicle re-identification, the models struggle in terms of real-world implementation because of their complexity and computational requirements. This is mainly due to the focus on computation-heavy feature extraction processes, along with complex pre-processing and post-processing steps. To address these issues, an approach incorporating content-based image retrieval techniques with deep neural models that are computationally efficient is proposed. The approach also considers relevance feedback during the post-processing phase. Experimental results reveal that the incorporation of relevance feedback technique as a post-processing technique in vehicle re-identification helps achieve significant improvement in terms of mean average precision and Rank@k. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.Content-based image retrievalDeep neural modelsRelevance feedbackVehicle re-identification