Performance Comparison of Five Methods Available in ImageJ for Bird Counting and Detection from Video Datasets

Abstract

Bird monitoring is an important approach to studying the diversity and abundance of birds, especially during migration, as it can provide core data for bird conservation purposes. The previous methods for bird number estimation are largely based on manual counting, which suffers from low throughput and a high error rate. In this study, we aimed to provide an alternative bird-counting method from video datasets by using five available ImageJ methods: Particle Analyzer, Find Maxima, Watershed segmentation, TrackMate, and trainable WEKA segmentation. The numbers of birds and their XY coordinates were extracted from videos to conduct a side-by-side comparison with the manual counting results, and the three important criteria of the sensitivity, precision, and F1 score were calculated for the performance evaluation. From the tests, which we conducted for four different cases with different bird numbers or flying patterns, TrackMate had the best overall performance for counting birds and pinpointing their locations, followed by Particle Analyzer, Find Maxima, WEKA, and lastly, Watershed, which showed low precision in most of the cases. In summary, five ImageJ-based counting methods were compared in this study, and we validated that TrackMate obtains the best performance for bird counting and detection.

Keywords

• bird counting
• ImageJ
• TrackMate
• video dataset