Abstract: Person re-identification has been an extremely chal-lenging task in computer vision which has been seen as a success with deep learning approaches. Despite successful models, there are gaps in the form of unbalanced labels, poor resolution, uncertain bounding box annotations, occlusions, and unlabelled datasets. Previous methods applied deep learning approaches based on feature representation, metric learning, and ranking optimization. In this work, we propose Global Average Attention Pooling (GAAP) on Resnet50 applied on four benchmark Re-ID datasets for classification tasks. We also perform an extensive evaluation on the proposed Attention module with different deep learning pipelines as backbone architecture. The four benchmark person Re-ID datasets used is Market-1501, RAiD, Partial-iLIDS, and RPIfield. We computed cumulative matching characteristics (CMC) and mean Average Precision (mAP) as the performance evaluation parameters of the proposed against the state of the art. The results obtained have shown that the added attention layer has improved the overall recognition precision over the baselines.

Syamala Kanchimani, Maloji Suman and P. V. V. Kishore, “Learning Global Average Attention Pooling (GAAP) on Resnet50 Backbone for Person Re-identification Problem” International Journal of Advanced Computer Science and Applications(IJACSA), 13(7), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130796

@article{Kanchimani2022,
title = {Learning Global Average Attention Pooling (GAAP) on Resnet50 Backbone for Person Re-identification Problem},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130796},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130796},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {7},
author = {Syamala Kanchimani and Maloji Suman and P. V. V. Kishore}
}