Vehicle Re-Identification involves identifying and matching a target vehicle with images captured from different views in a multi-camera network. This topic holds significant importance in various applications including intelligent transportation systems, video surveillance and smart city. However, Vehicle Re-Identification faces significant challenges in dynamic environments due to viewpoint variations, inter-vehicle appearance similarity, intra-class variability, illumination variation, occlusion and background clutter. To address these limitations, we propose HA-VReID, An Effective Hard Attention Model with Deep Learning for Vehicle Re-Identification that combines Hard attention mechanism for background removal and vehicle shape focus with EfficientNet-powered feature extraction for robust vehicle representation. Extensive experiments on the VeRi-776 and VRAI benchmarks demonstrate that our approach outperforms state-of-the-art methods in vehicle Re-Identification tasks.

Person re-identification is still an open challenging task in various fields due to numerous factors, including illumination changes, background clutter, pose state variations and cloth changes. Several approaches have been suggested to address this problem in the context of deep learning. Generative models, particularly Variational Autoencoders (VAEs), have emerged as promising tools to address these challenges by learning discriminative feature representations of individual images. In this paper, we present Soft-Attention based Person Re-Identification (SAPRI), a novel approach that combines VAEs with a supervised ReID method to enhance the resilience and efficacy of ReID systems. The proposed approach focuses on data reconstruction based on soft attention. Variational autoen-coders encode principally person data, while ignoring irrelevant information. By incorporating supervised ReID, the model learns to appropriately classify persons in real world environments. Our SAPRI proposed method has been evaluated on well-known benchmarks, DukeMTMC-reID and CUHK03, demonstrating superior performance compared to existing state-of-the-art techniques in terms of the mean Average Precision evaluation metric (mAP). Additionally, qualitative results show the effectiveness of the VAE in generating discriminative representations of person images.

Person Re-Identification is a critical component in modern video surveillance systems for locating individuals across cameras from various viewpoints. However, one of the significant challenges in person ReID arises when facial information is unavailable. To address this issue, we propose GF2PReID, a novel framework that leverages state-of-the-art deep learning-based ReID approaches to generate prior knowledge of the face region and provide detailed information about human body images. Our approach utilizes a deep residual network model trained with transfer learning to extract discriminative features from images with low discrimination, including low illumination and occlusion, collected from diverse datasets. Experimental results, on 2 challenging datasets: Market-1501 and CUHK03, demonstrate that our GF2PReID framework improves the datasets and significantly improves the performance of the Resnet-50 model, reaching the highest performance.