Abstract
In recent years, computer vision for automatically identification and tracking of animals has evolved into a popular tool for quantifying behavior. Honeybees are a popular model for learning and memory, so tracking of honeybees within a colony is a particularly task due to dense populations, similar target appearance and a significant portion of the colony frequently leaving the hive. In this paper we present a detection method based on improved three-frame difference method and VIBE algorithm and one tracking method based on Kalman filtering for honeybees tracking. We evaluate the performance of the proposed methods on datasets which contains videos with crowd honeybee colony. The experimental results show that the proposed method performs good performance in detection and tracking.
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Acknowledgements
This work was supported by Research Fund of SKL of Ocean Engineering in Shanghai Jiaotong University (1315; 1510), Research Fund of SKL of Marine Geology in Tongji University (MGK1608).
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Li, Y., Takahashi, S. (2020). Small Object Tracking in High Density Crowd Scenes. In: Lu, H. (eds) Cognitive Internet of Things: Frameworks, Tools and Applications. ISAIR 2018. Studies in Computational Intelligence, vol 810. Springer, Cham. https://doi.org/10.1007/978-3-030-04946-1_48
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DOI: https://doi.org/10.1007/978-3-030-04946-1_48
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