Optimizing Person Re-Identification Using Generated Attention Masks

• Type: Article in International Conference Proceedings Book
• Date: 2021

Authors

Capozzi, L (Author) Other; Pinto, JR (Author) Other; Jaime S Cardoso (Author) FEUP; Rebelo, A (Author) Other

Conference proceedings International

Title: CIARP

Pages: 248-257

Indexing

Scopus - 0 Citations

Other information

Authenticus ID: P-00V-YDZ

DOI: 10.1007/978-3-030-93420-0_24

Abstract (EN): The task of person re-identification has important applications in security and surveillance systems. It is a challenging problem since there can be a lot of differences between pictures belonging to the same person, such as lighting, camera position, variation in poses and occlusions. The use of Deep Learning has contributed greatly towards more effective and accurate systems. Many works use attention mechanisms to force the models to focus on less distinctive areas, in order to improve performance in situations where important information may be missing. This paper proposes a new, more flexible method for calculating these masks, using a U-Net which receives a picture and outputs a mask representing the most distinctive areas of the picture. Results show that the method achieves an accuracy comparable or superior to those in state-of-the-art methods.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 9