2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDMLR-APPL-IVSMR-3.5
Paper Title FINE-GRAINED MULTI-CLASS OBJECT COUNTING
Authors Hyojun Go, Junyoung Byun, Byeongjun Park, Myung-Ae Choi, Korea Advanced Institute of Science and Technology, Republic of Korea; Seunghwa Yoo, National Institute of Ecology, Republic of Korea; Changick Kim, Korea Advanced Institute of Science and Technology, Republic of Korea
SessionMLR-APPL-IVSMR-3: Machine learning for image and video sensing, modeling and representation 3
LocationArea D
Session Time:Wednesday, 22 September, 14:30 - 16:00
Presentation Time:Wednesday, 22 September, 14:30 - 16:00
Presentation Poster
Topic Applications of Machine Learning: Machine learning for image & video sensing, modeling, and representation
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Many animal species in the wild are at the risk of extinction. To deal with this situation, ecologists have monitored the population changes of endangered species. However, the current wildlife monitoring method is extremely laborious as the animals are counted manually. Automated counting of animals by species can facilitate this work and further renew the ways for ecological studies. However, to the best of our knowledge, few works and publicly available datasets have been proposed on multi-class object counting which is applicable to counting several animal species. In this paper, we propose a fine-grained multi-class object counting dataset, named KR-GRUIDAE, which contains endangered red-crowned crane and white-naped crane in the family Gruidae. We also propose a specialized network for multi-class object counting and line segment density maps, and show their effectiveness by comparing results of existing crowd counting methods on the KR-GRUIDAE dataset.