Improved Appearance Model for Handling Occlusion in Vehicle Tracking
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-7820-5_37
Reference29 articles.
1. Askar, W.A., Elmowafy, O., Ralescu, A., Youssif, A.A., Elnashar, G.A: Occlusion detection and processing using optical flow and particle filter. https://doi.org/10.1504/IJAIP.2020.10025745
2. Zhou, L., Zhang, J.: combined kalman filter and multifeature fusion siamese network for real-time visual tracking (2019). https://doi.org/10.3390/s19092201
3. Velazquez-Pupo, R., Sierra-Romero, A., Torres-Roman, D., Shkvarko, Y.V., Santiago-Paz, J., Gómez-Gutiérrez, D., Robles-Valdez, D., Hermosillo-Reynoso, F., Romero-Delgado, M.: Vehicle Detection with Occlusion Handling, Tracking, and OC-SVM Classification: A High Performance Vision-Based System (2018). https://doi.org/10.3390/s18020374
4. Zhang, T., Liu, S., Changsheng, X., Liu, B., Yang, M.-H.: Correlation particle filter for visual tracking (2017). https://doi.org/10.1109/TIP.2017.2781304
5. Mehmood, K., Jalil, A., Ali, A., Khan, B., Murad, M., Ullah Khan, W., He, Y.: Context-aware and occlusion handling mechanism for online visual object tracking. Journal (2021). https://doi.org/10.3390/electronics10010043
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