1. IEEE Signal Processing Magazine
2. Signal Processing Digital Library*
3. Inside Signal Processing Newsletter
4. SPS Resource Center
5. Career advancement & recognition
6. Discounts on conferences and publications
7. Professional networking
8. Communities for students, young professionals, and women
9. Volunteer opportunities
10. Coming soon! PDH/CEU credits
Click here to learn more.
In this paper, we present a spatial-temporal attention-aware learning (STAL) method for video-based person re-identification. Most existing person re-identification methods aggregate image features identically to represent persons, which are extracted from the same receptive field across video frames. However, the image quality may be varying for different spatial regions and changing over time, which shall contribute to person representation and matching adaptively. Our STAL method aims to attend to the salient parts of persons in videos jointly in both spatial and temporal domains. To achieve this, we slice the video into multiple spatial-temporal units which preserve the body structure of a person and develop a joint spatial-temporal attention model to learn the quality scores of these units. We evaluate the proposed method on three challenging datasets including iLIDS-VID, PRID-2011, and the large-scale MARS dataset, and consistently improve the rank-1 accuracy by a large margin of 5.7%, 0.9%, and 6.6% respectively, in comparison with the state-of-the-art methods.
© Copyright 2020 IEEE – All rights reserved. Use of this website signifies your agreement to the IEEE Terms and Conditions.
A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.