A Rule-Based Method for Automated Footprint Localization and Classification of Small Species

In environmental surveillance, ecology experts use a standard tracking tunnel system to acquire tracks or footprints of small animals, so that they can easily measure the presence of any selected animals or detect threatened species based on the manual analysis of gathered tracks. Distinguishing morphologically similar species through analysing footprints requires a great amount of efforts on observation, even experienced wildlife experts can not accomplish this task with highly reliable results. In recent years, image processing technology has become a model example for applying computer science technology to many other study areas or industries, in order to improve accuracy, productivity, and reliability. In this paper, we demonstrate a model/rule-based method for automated footprint identification which includes localization and classification of small species. With appropriate developments or modifications, this method has certain potential for automated identification of any species.