Cattle behavior recognition based on feature fusion under a dual attention mechanism
Tài liệu tham khảo
Taylor, 2000, Protozoal disease in cattle and sheep, Practice, 22, 604, 10.1136/inpract.22.10.604
Choutas, V. , Weinzaepfel, P. , Revaud, J. , & Schmid, C. . (2018). PoTion: Pose MoTion Representation for Action Recognition. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE.
Gao, R. , Xiong, B. , & Grauman, K. . (2017). Im2flow: motion hallucination from static images for action recognition.
Feichtenhofer, 2018
Wang, X. , Girshick, R. , Gupta, A. , & He, K. . (2017). Non-local neural networks.
Yang, 2017
Li, W. , Wang, Z. , Yin, B. , Peng, Q. , Du, Y. , & Xiao, T. , et al. (2019). Rethinking on multi-stage networks for human pose estimation.
Iandola, F. N. , Han, S. , Moskewicz, M. W. , Ashraf, K. , Dally, W. J. , & Keutzer, K. . (2016). Squeezenet: alexnet-level accuracy with 50x fewer parameters and <0.5mb model size.
Chollet, F. . (2017). Xception: Deep Learning with Depthwise Separable Convolutions. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE.
Howard, A. G. , Zhu, M. , Chen, B. , Kalenichenko, D. , Wang, W. , & Weyand, T. , et al. (2017). Mobilenets: efficient convolutional neural networks for mobile vision applications.
Zhang, X. , Zhou, X. , Lin, M. , & Sun, J. . (2017). Shufflenet: an extremely efficient convolutional neural network for mobile devices.
Sandler, 2018, Mobilenetv2: inverted residuals and linear bottlenecks, IEEE.
Ma, 2018
Howard, A. , Sandler, M. , Chu, G. , Chen, L. C. , Chen, B. , & Tan, M. , et al. (2019). Searching for mobilenetv3.
Roy, 2018
Woo, S. , Park, J. , Lee, J. Y. , & Kweon, I. S. . (2018). CBAM: Convolutional Block Attention Module. European Conference on Computer Vision. Springer, Cham.
Zin, T. T., Misawa, S., Pwint, M. Z., Thant, S., Seint, P. T., Sumi, K., & Yoshida, K. (2020, March). Cow Identification System using Ear Tag Recognition. In2020 IEEE 2nd Global Conference on Life Sciences and Technologies (LifeTech)(pp. 65-66). IEEE.
Ng, M. L., Leong, K. S., Hall, D. M., & Cole, P. H. (2005, August). A small passive UHF RFID tag for livestock identification. In2005 IEEE International Symposium on Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications(Vol. 1, pp. 67-70). IEEE.
Ketprom, U., Mitrpant, C., Makhapun, P., Makwimanloy, S., & Laokok, S. (2011, March). RFID for cattle traceability system at animal checkpoint. In2011 Annual SRII Global Conference(pp. 517-521). IEEE.
Wang, Z., Fu, Z., Chen, W., & Hu, J. (2010, October). A RFID-based traceability system for cattle breeding in China. In2010 International Conference on Computer Application and System Modeling (ICCASM 2010)(Vol. 2, pp. V2-567). IEEE.
Volk, T., & Jansen, D. (2012, June). Implantable RFID sensor platform to monitor vital functions of small animals controlled by network based software. InSmart SysTech 2012; European Conference on Smart Objects, Systems and Technologies(pp. 1-6). VDE.
Qiao, Y., Su, D., Kong, H., Sukkarieh, S., Lomax, S., & Clark, C. (2020, August). BiLSTM-based individual cattle identification for automated precision livestock farming. In2020 IEEE 16th International Conference on Automation Science and Engineering (CASE)(pp. 967-972). IEEE.
Qiao, 2019, Individual cattle identification using a deep learning based framework, IFAC-PapersOnLine, 52, 318, 10.1016/j.ifacol.2019.12.558
Zin, T. T., Phyo, C. N., Tin, P., Hama, H., & Kobayashi, I. (2018, March). Image technology based cow identification system using deep learning. InProceedings of the International MultiConference of Engineers and Computer Scientists(Vol. 1, pp. 236-247).
Qin, 2019, Pig face recognition algorithm based on bilinear convolution neural network, Journal of Hangzhou Dianzi University, 39, 12
He, D. J., Liu, J. M., Xiong, H. T., Lu, Z. Z. . (2020). “Individual Identification of Dairy Cows Based on Improved YOLO v3”, Transactions of the Chinese Society for Agricultural Machinery, vol. 51,no. 4 ,pp. 250-260, 2020.
Tang, 2019, Salient object detection of dairy goats in farm image based on background and foreground priors, Neurocomputing, 332, 270, 10.1016/j.neucom.2018.12.052
Dong, 2018, Dairy goat detection based on faster r-cnn from surveillance video, Computers and Electronics in Agriculture, 154, 443, 10.1016/j.compag.2018.09.030
Lin, T. Y., Goyal, P., Girshick, R., He, K., & Dollár, P. (2017). Focal loss for dense object detection. InProceedings of the IEEE international conference on computer vision(pp. 2980-2988).
Zhong, 2021, Attention-guided Image Captioning with Adaptive Global and Local Feature Fusion, Journal of Visual Communication and Image Representation, 78, 103138, 10.1016/j.jvcir.2021.103138
Tian, 2019, Weighted correlation filters guidance with spatial-temporal attention for online multi-object tracking, Journal of Visual Communication and Image Representation, 63, 102576, 10.1016/j.jvcir.2019.102576
Jiang, 2020, Spatial-temporal saliency action mask attention network for action recognition, Journal of Visual Communication and Image Representation, 71, 102846, 10.1016/j.jvcir.2020.102846
Su, 2020, Convolutional neural network with adaptive inferential framework for skeleton-based action recognition, Journal of Visual Communication and Image Representation, 73
Zhang, 2020, SAR-NAS: Skeleton-based action recognition via neural architecture searching, Journal of Visual Communication and Image Representation, 73, 102942, 10.1016/j.jvcir.2020.102942
Delaitre, 2010, Recognizing human actions in still images: a study of bag-of-features and part-based representations, British Machine Vision Conference. DBLP.