TY - JOUR
AU - Fayek, Magda B.
AB - Video-based person re-identification has become quite attractive due to its importance in many vision surveillance problems. It is a challenging topic due to the inter/intra changes, occlusion, and pose variations involved. In this paper, we propose a pyramid-attentive framework that relies on multi-part features and multiple attention to aggregate features of multi-levels and learns attention-based representations of persons through various aspects. Self-attention is used to strengthen the most discriminative features in the spatial and channel domains and hence capture robust global information. We propose the use of part-relation attention between different multi-granularities of features’ representation to focus on learning appropriate local features. Temporal attention is used to aggregate temporal features. We integrate the most robust features in the global and multi-level views to build an effective convolution neural network (CNN) model. The proposed model outperforms the previous state-of-the art models on three datasets. Notably, using the proposed model enables the achievement of 98.9% (a relative improvement of 2.7% on the GRL) top1 accuracy and 99.3% mAP on the PRID2011, and 92.8% (a relative improvement of 2.4% relative to GRL) top1 accuracy on iLIDS-vid. We also explore the generalization ability of our model on a cross dataset.
TI - Person Re-Identification via Pyramid Multipart Features and Multi-Attention Framework
JF - Big Data and Cognitive Computing
DO - 10.3390/bdcc6010020
DA - 2022-02-09
UR - https://www.deepdyve.com/lp/multidisciplinary-digital-publishing-institute/person-re-identification-via-pyramid-multipart-features-and-multi-qwFfmkg8Yd
SP - 20
VL - 6
IS - 1
DP - DeepDyve
ER -