Automatic Detection and Tracking of Maneuverable Birds in Videos

Abstract

In this paper, we try to detect and track maneuverable birds in captured videos for further automatic research. To avoid abrupt scene change, videos are captured by pointing the camera upwards and making the image against the sky. Two levels (pixel level and frame level) background update algorithm is used to get the foreground in real time. In frame level, three thresholds are used to update the background to achieve fast background update under abrupt scene changing. Targets are detected from the binary foreground after retrieving contours, size filter and margin measure. The tracking of maneuverable birds is achieved by using a Markov Chain Monte Carlo (MCMC) filter with no move types. The experiment results show that multiple maneuverable birds are detected and tracked accurately in real time, and the size of the tracking box adjust fast to cover the true area of birds.