Deep Learning Methods for Multi-Species Animal Re-identification and Tracking – a Survey

Autor: Prashanth C. Ravoor, Sudarshan T.S.B.
Rok vydání: 2020
Předmět:
Zdroj: Computer Science Review. 38:100289
ISSN: 1574-0137
DOI: 10.1016/j.cosrev.2020.100289
Popis: Technology has an important part to play in wildlife and ecosystem conservation, and can vastly reduce time and effort spent in the associated tasks. Deep learning methods for computer vision in particular show good performance on a variety of tasks; animal detection and classification using deep learning networks are widely used to assist ecological studies. A related challenge is tracking animal movement over multiple cameras. For effective animal movement tracking, it is necessary to distinguish between individuals of the same species to correctly identify an individual moving between two cameras. Such problems could potentially be solved through animal re-identification methods. In this paper, the applicability of existing animal re-identification techniques for fully automated individual animal tracking in a cross-camera setup is explored. Recent developments in animal re-identification in the context of open-set recognition of individuals, and the extension of these systems to multiple species is examined. Some of the best performing human re-identification and object tracking systems are also reviewed in view of extending ideas within them to individual animal tracking. The survey concludes by presenting common trends in re-identification methods, lists a few challenges in the domain and recommends possible solutions.
Databáze: OpenAIRE
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