Automatic Bird Species Detection From Crowd Sourced Videos

To assist nature observation, we develop two algorithms to enable automatic bird species filtering using crowd sourced videos as inputs where camera motion and parameters are often unknown. The first algorithm recognizes the time series of salient extremities, which is the inter-wing tip distance (IWTD), from motion segmented bird contours. To analyze the feasibility of the proposed algorithm, we derive the probability that the salient extremity can be recognized from a video captured by an arbitrary camera with unknown parameters. We also prove that the periodicity of the IWTD in the image is the same as the wingbeat frequency in the 3D space regardless of camera parameters with the exception of ignorable degenerated cases. Therefore, the second algorithm applies Fast Fourier Transform to the series and classifies bird species using likelihood ratios. The algorithm outputs a ranked list of likelihood of candidate species. Experiment results validate our analysis and show that the algorithm is very robust to segmentation error and data loss up to 30%.