Person Re-Identification_(ReID) is a crucial task in computer vision with growing importance in security and engineering applications, particularly in surveillance and smart city systems. The hand-crafted feature-based existing approaches that consider texture and color struggle with complex real challenges involving lighting; person pose; and variable backgrounds. This survey offers an updated and focused review of deep learning-based ReID methods, encompassing research from 2020 to 2025. It investigates in-depth the engineering aspects, including system integration, real-time performance, and sensor constraints, which are often overlooked in reviews of earlier work. Techniques discussed in this study involve CNNs and transformers, triplet loss and contrastive learning, GANs, and methods that enhance matching accuracy and generalization. The paper compares recent methods; presenting their strengths and weaknesses, and setting directions for future research. The survey aims to provide a practical reference for engineers and researchers interested in developing robust and scalable ReID systems in real-world environments
Z.A.M. Faisal, Nidhal K. El Abbadi· Kufa journal of Engineering· 0 citations
Color texture classification is an important task in computer vision, with applications in medical imaging, industrial inspection, remote sensing, and material analysis. This paper presents a hybrid framework that integrates Principal Component Analysis (PCA), Local Binary Patterns (LBPs), Wavelet Scattering Transform, and the XGBoost classifier for color texture classification. The proposed pipeline first performs image pre-processing, including resizing and denoising, followed by channel-wise feature extraction using LBP and Wavelet Scattering Transform on the Red, Green, and Blue channels independently. Then, the obtained feature vectors were concatenated, and PCA was applied on the fused feature space for dimensionality reduction and redundancy elimination before proceeding to XGBoost classification. This method not only leverages complementary information of Chroma and texture information but also achieves reduced dimensionality and computational burden. The finally optimized features were input into the XGBoost classifier for color texture classification, which is good at fitting non-linear dependency and includes a regularization to generalize better. Our proposed framework was tested on three benchmark color texture datasets: KTH-TIPS, Outex_10, and VisTex. Experimental results have demonstrated that on these three datasets, the average performance reaches 98.0% accuracy, 0.981 precision, 0.981 recall, and 0.979 F1-score, respectively. It demonstrates that the two selected complementary feature extraction methods provide a compact yet effective representation for color texture classification on these datasets. It is expected that the proposed framework serves as an efficient combination of established methods and as a good competitive baseline for color texture analysis. Future works will consider applying it to larger color texture datasets for general verification, enhancing its computational efficiency and automating the parameter selection process.
Zahoor M. Aydam, Baidaa Mutasher Rashed, Nidhal K. El Abbadi· Journal of Imaging· 0 citations