Smart City Gnosys
Smart city article details
| Title | Deep-Reid: Deep Features And Autoencoder Assisted Image Patching Strategy For Person Re-Identification In Smart Cities Surveillance |
|---|---|
| ID_Doc | 18110 |
| Authors | Khan S.U.; Hussain T.; Ullah A.; Baik S.W. |
| Year | 2024 |
| Published | Multimedia Tools and Applications, 83, 5 |
| DOI | http://dx.doi.org/10.1007/s11042-020-10145-8 |
| Abstract | 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. © 2021, Springer Science+Business Media, LLC, part of Springer Nature. |
| Author Keywords | Deep autoencoder; Deep learning; Hybrid CNN; Learned features extraction; Person re-identification; Smart surveillance |
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