Turkey production is very important in the United States. Production can be impacted by turkey welfare. However, changes in turkey welfare might not be completely visible to humans. Therefore, we work on building a robust system that is capable of tracking turkeys and identifying different types of aggregated behavior. By successfully analyzing and detecting changes in turkey welfare, farmers or researchers can take appropriate actions before problems further escalate.
Publication:
S. Ju, M. A. Erasmus, A. R. Reibman, F. Zhu, “Video Tracking to Monitor Turkey Welfare,” Proceedings of IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI), Santa Fe, NM, USA, Mar 2020.
S. Ju, S. Mahapatra, M.A. Erasmus, A.R. Reibman, F. Zhu, “Turkey Behavior Identification System with a GUI Using Deep Learning and Video Analytics,” Electronic Imaging, virtual, Jan 2021.
S. Ju, M.A. Erasmus, F. Zhu, A.R. Reibman, “Turkey Behavior Identification using Video Analytics and Object Tracking,” Proceedings of the IEEE International Conference on Image Processing (ICIP), virtual, Sep 2021.
Y. Dong, G.S. Fraley, J.M. Siegford, F. Zhu, M.A. Erasmus, “Comparing different environmental enrichments for improving the welfare and walking ability of male turkeys,” PLOS ONE, Apr 2023.
Motion-sensor camera traps help collect images of animals in the wild without intruding upon their native habitat. To obtain key insights about animal health and population densities, accurate counting, detection and classification of animals is important. The goal of this projecgt is to take camera trap images as inputs and produce automatically a list of identified species, their count, and observed behaviors for each animal along with a timeline of the occurrence of these activities.
Publication:
P. Singh, S.M. Lindshield, F. Zhu, A. R. Reibman, “Animal Localization in Camera-Trap Images with Complex Background,” Proceedings of IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI), Santa Fe, NM, USA, Mar 2020.