Fine-Grained Multi-Class Object Counting

• Published in: 2021 IEEE International Conference on Image Processing (ICIP) pp. 509 - 513
• Main Authors: Go, Hyojun, Byun, Junyoung, Park, Byeongjun, Choi, Myung-Ae, Yoo, Seunghwa, Kim, Changick
• Format: Conference Proceeding
• Language: English
• Published: IEEE 19.09.2021
• Subjects: Benchmark testing; Conferences; Counting for Animal conservation; Counting with classification; Cranes; Crowd Counting; Image processing; Multi-class object counting; Multi-class object counting dataset; Network architecture; Sociology; Wildlife
• ISSN: 2381-8549
• DOI: 10.1109/ICIP42928.2021.9506384

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.