Research Portal

A benchmark database for animal re-identification and tracking

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Standard Standard

A benchmark database for animal re-identification and tracking. / Kuncheva, Ludmila; Hennessey, Samuel; Williams, Francis et al.
Proc. of the Fifth IEEE International Conference on Image Processing, Applications and Systems (IPAS). IEEE, 2023.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

HarvardHarvard

Kuncheva, L, Hennessey, S, Williams, F & Rodriguez, J 2023, A benchmark database for animal re-identification and tracking. in Proc. of the Fifth IEEE International Conference on Image Processing, Applications and Systems (IPAS). IEEE. https://doi.org/10.1109/IPAS55744.2022.10052988

APA

Kuncheva, L., Hennessey, S., Williams, F., & Rodriguez, J. (2023). A benchmark database for animal re-identification and tracking. In Proc. of the Fifth IEEE International Conference on Image Processing, Applications and Systems (IPAS) IEEE. https://doi.org/10.1109/IPAS55744.2022.10052988

CBE

Kuncheva L, Hennessey S, Williams F, Rodriguez J. 2023. A benchmark database for animal re-identification and tracking. In Proc. of the Fifth IEEE International Conference on Image Processing, Applications and Systems (IPAS). IEEE. https://doi.org/10.1109/IPAS55744.2022.10052988

MLA

Kuncheva, Ludmila et al. "A benchmark database for animal re-identification and tracking". Proc. of the Fifth IEEE International Conference on Image Processing, Applications and Systems (IPAS). IEEE. 2023. https://doi.org/10.1109/IPAS55744.2022.10052988

VancouverVancouver

Kuncheva L, Hennessey S, Williams F, Rodriguez J. A benchmark database for animal re-identification and tracking. In Proc. of the Fifth IEEE International Conference on Image Processing, Applications and Systems (IPAS). IEEE. 2023 doi: 10.1109/IPAS55744.2022.10052988

Author

Kuncheva, Ludmila ; Hennessey, Samuel ; Williams, Francis et al. / A benchmark database for animal re-identification and tracking. Proc. of the Fifth IEEE International Conference on Image Processing, Applications and Systems (IPAS). IEEE, 2023.

RIS

TY - GEN

T1 - A benchmark database for animal re-identification and tracking

AU - Kuncheva, Ludmila

AU - Hennessey, Samuel

AU - Williams, Francis

AU - Rodriguez, Juan

PY - 2023/12/6

Y1 - 2023/12/6

N2 - While there are multiple sources of annotated images and videos for human and vehicle re-identification, databases for individual animal recognition are still in demand. We present a database containing five annotated video clips each containing between 9 and 27 identities. The overall number of individual animals is 20,490, and the total number of classes is 93. The database can be used for testing novel methods for animal reidentification, object detection and tracking. The main challenge of the database is that multiple animals are present in the same video frame, leading to problems with occlusion and noisy, cluttered bounding boxes. To set-up a benchmark on individual animal recognition, we trained and tested 26 classification methods for the five videos and three feature representations. We also report results with state-of-the-art deep learning methods for object detection (MMDet) and tracking (Uni-Track).

AB - While there are multiple sources of annotated images and videos for human and vehicle re-identification, databases for individual animal recognition are still in demand. We present a database containing five annotated video clips each containing between 9 and 27 identities. The overall number of individual animals is 20,490, and the total number of classes is 93. The database can be used for testing novel methods for animal reidentification, object detection and tracking. The main challenge of the database is that multiple animals are present in the same video frame, leading to problems with occlusion and noisy, cluttered bounding boxes. To set-up a benchmark on individual animal recognition, we trained and tested 26 classification methods for the five videos and three feature representations. We also report results with state-of-the-art deep learning methods for object detection (MMDet) and tracking (Uni-Track).

KW - Animal re-identification

KW - Benchmark database

KW - Classification of images

KW - Object detection and tracking

U2 - 10.1109/IPAS55744.2022.10052988

DO - 10.1109/IPAS55744.2022.10052988

M3 - Conference contribution

BT - Proc. of the Fifth IEEE International Conference on Image Processing, Applications and Systems (IPAS)

PB - IEEE

ER -