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

Samee Ullah Khan, Tanveer Hussain, Amin Ullah, Sung Wook Baik

Research output: Contribution to journalArticle (journal)peer-review

16 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)1-22
JournalMultimedia Tools and Applications
DOIs
Publication statusPublished - 22 Jan 2021

Keywords

  • Smart surveillance
  • Person re-identification
  • Deep autoencoder
  • Deep learning
  • Learned features extraction
  • Hybrid CNN

Fingerprint

Dive into the research topics of 'Deep-ReID: deep features and autoencoder assisted image patching strategy for person re-identification in smart cities surveillance'. Together they form a unique fingerprint.

Cite this