Deep-ReID: deep features and autoencoder assisted image patching strategy for person re-identification in smart cities surveillance

• Published in: Multimedia tools and applications Vol. 83; no. 5; pp. 15079 - 15100
• Main Authors: Khan, Samee Ullah, Hussain, Tanveer, Ullah, Amin, Baik, Sung Wook
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.02.2024; Springer Nature B.V
• Subjects: 1158T: Role of Computer Vision in Smart Cities: Applications and Research Challenges; Annotations; Artificial neural networks; Cities; Complexity; Computer Communication Networks; Computer Science; Data Structures and Information Theory; Feature maps; Image acquisition; Machine learning; Multimedia Information Systems; Patching; Smart cities; Special Purpose and Application-Based Systems; Supervised learning; Surveillance; Deep learning; Learned features extraction; Hybrid CNN; Smart surveillance; Person re-identification; Deep autoencoder
• ISSN: 1573-7721; 1380-7501; 1573-7721
• DOI: 10.1007/s11042-020-10145-8

Person Re-identification (P-ReID) task searches for true matches of a given query from a large repository of non-overlapping camera’s images/videos. In smart cities surveillance, P-ReID is challenging due to variation in human’s appearance, illumination affects, and difference in viewpoints. The mainstream approaches achieve P-ReID by implementing supervised learning strategies, requiring exhaustive manual annotation, which is probably erroneous due to human involvement. In addition, the employed methods use high-dimensional feature maps to identify a person, which is not a realistic approach in terms of storage resources and computational complexity. To tackle these issues, we incorporate learned features and deep autoencoder under the umbrella of a unified framework for P-ReID. First, we apply a unique image patching strategy by dividing the input image into two parts (upper and lower) and acquire learned features from fully connected layer of a pretrained Convolutional Neural Network (CNN) model for both patches. To achieve efficient and high performance, the proposed framework utilizes a self-tuned autoencoder to acquire low-dimensional representative features. The obtained features are matched with the patterns of database via cosine similarity measurement to re-identify a person’s appearance. The proposed framework provides a trade-off between time complexity and accuracy, where a lightweight model can be incorporated with reduced number of autoencoder layers to obtain fast and comparatively flexible results. The major novelty of the proposed framework includes implementation of a hybrid network mechanism for P-ReID, which shows convincing real-time results and best fits for smart cities surveillance. The proposed framework is tested over several P-ReID datasets to prove its influence over the existing works with reduced computational complexity.