Robust single target tracking using determinantal point process observations

Bolme, D. S., Beveridge, J. R., Draper, B. A. and Lui, Y. M. 2010. Visual object tracking using adaptive correlation filters. 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, pp. 2544–2550. Bolme D. S. Beveridge J. R. Draper B. A. and Lui Y. M. 2010 Visual object tracking using adaptive correlation filters 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE , pp. 2544 – 2550 Search in Google Scholar

Czyz, J., Ristic, B. and Macq, B. 2007. A particle filter for joint detection and tracking of color objects. Image and Vision Computing 25(8): 1271–1281. Czyz J. Ristic B. and Macq B. 2007 A particle filter for joint detection and tracking of color objects Image and Vision Computing 25 (8) 1271 – 1281 Search in Google Scholar

García-Fernández, A. F., Williams, J. L., Granström, K. and Svensson, L. 2018. Poisson multi-bernoulli mixture filter: direct derivation and implementation. IEEE Transactions on Aerospace and Electronic Systems 54 August, pp. 1883–1901. García-Fernández A. F. Williams J. L. Granström K. and Svensson L. 2018 Poisson multi-bernoulli mixture filter: direct derivation and implementation IEEE Transactions on Aerospace and Electronic Systems 54 August , pp. 1883 – 1901 Search in Google Scholar

Hare, S., Golodetz, S., Saffari, A., Vineet, V., Cheng, M.-M., Hicks, S. L. and Torr, P. H. 2016. Struck: structured output tracking with kernels. IEEE Transactions on Pattern Analysis and Machine Intelligence 38(10): 2096–2109. Hare S. Golodetz S. Saffari A. Vineet V. Cheng M.-M. Hicks S. L. and Torr P. H. 2016 Struck: structured output tracking with kernels IEEE Transactions on Pattern Analysis and Machine Intelligence 38 (10) 2096 – 2109 Search in Google Scholar

Henriques, J. F., Caseiro, R., Martins, P. and Batista, J. 2015. High-speed tracking with kernelized correlation filters. IEEE Transactions on Pattern Analysis and Machine Intelligence 37(3): 583–596. Henriques J. F. Caseiro R. Martins P. and Batista J. 2015 High-speed tracking with kernelized correlation filters IEEE Transactions on Pattern Analysis and Machine Intelligence 37 (3) 583 – 596 Search in Google Scholar

Hoseinnezhad, R., Vo, B. N. and Vo, B. T. 2013. Visual tracking in background subtracted image sequences via multi-bernoulli filtering. IEEE Transactions on Signal Processing 61 January, pp. 392–397. Hoseinnezhad R. Vo B. N. and Vo B. T. 2013 Visual tracking in background subtracted image sequences via multi-bernoulli filtering IEEE Transactions on Signal Processing 61 January , pp. 392 – 397 Search in Google Scholar

Hoseinnezhad, R., Vo, B.-N., Vo, B.-T. and Suter, D. 2012. Visual tracking of numerous targets via multibernoulli filtering of image data. Pattern Recognition 45(10): 3625–3635. Hoseinnezhad R. Vo B.-N. Vo B.-T. and Suter D. 2012 Visual tracking of numerous targets via multibernoulli filtering of image data Pattern Recognition 45 (10) 3625 – 3635 Search in Google Scholar

Jorquera, F., Hernández, S. and Vergara, D. 2017. Multi target tracking using determinantal point processes. in Mendoza, M. and Velastin, S. (Eds), Iberoamerican Congress on Pattern Recognition Springer International Publishing, Cham, pp. 323–330. Jorquera F. Hernández S. and Vergara D. 2017 Multi target tracking using determinantal point processes . in Mendoza M . and Velastin S. (Eds), Iberoamerican Congress on Pattern Recognition Springer International Publishing, Cham , pp. 323 – 330 Search in Google Scholar

Jorquera, F., Hernández, S. and Vergara, D. 2019. Probability hypothesis density filter using determinantal point processes for multi object tracking. Computer Vision and Image Understanding 183: 33–41. Jorquera F. Hernández S. and Vergara D. 2019 Probability hypothesis density filter using determinantal point processes for multi object tracking Computer Vision and Image Understanding 183 33 – 41 Search in Google Scholar

Kingman, J. F. C. 1993. Poisson Processes, Clarendon Press, Oxford. Kingman J. F. C. 1993 Poisson Processes Clarendon Press, Oxford Search in Google Scholar

Kristan, M., Leonardis, A., Matas, J., Felsberg, M., Pflugfelder, R., Zajc, L. Č., Vojír, T., Bhat, G., Lukežič, A., Eldesokey, A., Fernández, G., García-Martín, Á., Iglesias-Arias, Á., Alatan, A. A., González-García, A., Petrosino, A., Memarmoghadam, A., Vedaldi, A., Muhič, A., He, A., Smeulders, A., Perera, A. G., Li, B., Chen, B., Kim, C., Xu, C., Xiong, C., Tian, C., Luo, C., Sun, C., Hao, C., Kim, D., Mishra, D., Chen, D., Wang, D., Wee, D., Gavves, E., Gundogdu, E., Velasco-Salido, E., Khan, F. S., Yang, F., Zhao, F., Li, F., Battistone, F., De Ath, G., Subrahmanyam, G. R. K. S., Bastos, G., Ling, H., Galoogahi, H. K., Lee, H., Li, H., Zhao, H., Fan, H., Zhang, H., Possegger, H., Li, H., Lu, H., Zhi, H., Li, H., Lee, H., Chang, H. J., Drummond, I., Valmadre, J., Martin, J. S., Chahl, J., Choi, J. Y., Li, J., Wang, J., Qi, J., Sung, J., Johnander, J., Henriques, J., Choi, J., van de Weijer, J., Herranz, J. R., Martínez, J. M., Kittler, J., Zhuang, J., Gao, J., Grm, K., Zhang, L., Wang, L., Yang, L., Rout, L., Si, L., Bertinetto, L., Chu, L., Che, M., Maresca, M. E., Danelljan, M., Yang, M.-H., Abdelpakey, M., Shehata, M., Kang, M., Lee, N., Wang, N., Miksik, O., Moallem, P., Vicente-Moñivar, P., Senna, P., Li, P., Torr, P., Raju, P. M., Ruihe, Q., Wang, Q., Zhou, Q., Guo, Q., Martín-Nieto, R., Gorthi, R. K., Tao, R., Bowden, R., Everson, R., Wang, R., Yun, S., Choi, S., Vivas, S., Bai, S., Huang, S., Wu, S., Hadfield, S., Wang, S., Golodetz, S., Ming, T., Xu, T., Zhang, T., Fischer, T., Santopietro, V., Štruc, V., Wei, W., Zuo, W., Feng, W., Wu, W., Zou, W., Hu, W., Zhou, W., Zeng, W., Zhang, X., Wu, X., Wu, X.-J., Tian, X., Li, Y., Lu, Y., Law, Y. W., Wu, Y., Demiris, Y., Yang, Y., Jiao, Y., Li, Y., Zhang, Y., Sun, Y., Zhang, Z., Zhu, Z., Feng, Z.-H., Wang, Z. and He, Z. 2019. The sixth visual object tracking vot2018 challenge results. in Leal-Taixé, L. and Roth, S. (Eds), Computer Vision – ECCV 2018 Workshops Springer International Publishing, Cham, pp. 3–53. Kristan M. Leonardis A. Matas J. Felsberg M. Pflugfelder R. Zajc L. Č. Vojír T. Bhat G. Lukežič A. Eldesokey A. Fernández G. García-Martín Á. Iglesias-Arias Á. Alatan A. A. González-García A. Petrosino A. Memarmoghadam A. Vedaldi A. Muhič A. He A. Smeulders A. Perera A. G. Li B. Chen B. Kim C. Xu C. Xiong C. Tian C. Luo C. Sun C. Hao C. Kim D. Mishra D. Chen D. Wang D. Wee D. Gavves E. Gundogdu E. Velasco-Salido E. Khan F. S. Yang F. Zhao F. Li F. Battistone F. De Ath G. Subrahmanyam G. R. K. S. Bastos G. Ling H. Galoogahi H. K. Lee H. Li H. Zhao H. Fan H. Zhang H. Possegger H. Li H. Lu H. Zhi H. Li H. Lee H. Chang H. J. Drummond I. Valmadre J. Martin J. S. Chahl J. Choi J. Y. Li J. Wang J. Qi J. Sung J. Johnander J. Henriques J. Choi J. van de Weijer J. Herranz J. R. Martínez J. M. Kittler J. Zhuang J. Gao J. Grm K. Zhang L. Wang L. Yang L. Rout L. Si L. Bertinetto L. Chu L. Che M. Maresca M. E. Danelljan M. Yang M.-H. Abdelpakey M. Shehata M. Kang M. Lee N. Wang N. Miksik O. Moallem P. Vicente-Moñivar P. Senna P. Li P. Torr P. Raju P. M. Ruihe Q. Wang Q. Zhou Q. Guo Q. Martín-Nieto R. Gorthi R. K. Tao R. Bowden R. Everson R. Wang R. Yun S. Choi S. Vivas S. Bai S. Huang S. Wu S. Hadfield S. Wang S. Golodetz S. Ming T. Xu T. Zhang T. Fischer T. Santopietro V. Štruc V. Wei W. Zuo W. Feng W. Wu W. Zou W. Hu W. Zhou W. Zeng W. Zhang X. Wu X. Wu X.-J. Tian X. Li Y. Lu Y. Law Y. W. Wu Y. Demiris Y. Yang Y. Jiao Y. Li Y. Zhang Y. Sun Y. Zhang Z. Zhu Z. Feng Z.-H. Wang Z. and He Z. 2019 The sixth visual object tracking vot2018 challenge results . in Leal-Taixé L. and Roth S. (Eds), Computer Vision – ECCV 2018 Workshops Springer International Publishing Cham , pp. 3 – 53 Search in Google Scholar

Kulesza, A. and Taskar, B. 2011. Learning determinantal point processes. Proceedings of the Twenty-Seventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-11), AUAI Press, Corvallis, OR, pp. 419–427. Kulesza A. and Taskar B. 2011 Learning determinantal point processes Proceedings of the Twenty-Seventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-11), AUAI Press Corvallis, OR , pp. 419 – 427 Search in Google Scholar

Lee, D., Cha, G., Yang, M.-H. and Oh, S. 2016. Individualness and determinantal point processes for pedestrian detection. in Leibe, B., Matas, J., Sebe, N. and Welling, M. (Eds), European Conference on Computer Vision Springer International Publishing, Cham, pp. 330–346. Lee D. Cha G. Yang M.-H. and Oh S. 2016 Individualness and determinantal point processes for pedestrian detection . in Leibe B. Matas J. Sebe N . and Welling M . (Eds), European Conference on Computer Vision Springer International Publishing, Cham , pp. 330 – 346 Search in Google Scholar

Li, B., Yan, J., Wu, W., Zhu, Z. and Hu, X. 2018. High performance visual tracking with siamese region proposal network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8971–8980. Li B. Yan J. Wu W. Zhu Z. and Hu X. 2018 High performance visual tracking with siamese region proposal network Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , pp. 8971 – 8980 Search in Google Scholar

Li, P., Wang, D., Wang, L. and Lu, H. 2018. Deep visual tracking: review and experimental comparison. Pattern Recognition 76: 323–338. Li P. Wang D. Wang L. and Lu H. 2018 Deep visual tracking: review and experimental comparison Pattern Recognition 76 323 – 338 Search in Google Scholar

Maggio, E. and Cavallaro, A. 2011. Video Tracking: Theory and Practice, John Wiley & Sons, Bridgewater, NJ. Maggio E. and Cavallaro A. 2011 Video Tracking: Theory and Practice John Wiley & Sons Bridgewater, NJ Search in Google Scholar

Mahler, R. 2007. Phd filters of higher order in target number. IEEE Transactions on Aerospace and Electronic Systems 43(4): pp. 1523–1543. Mahler R. 2007 Phd filters of higher order in target number IEEE Transactions on Aerospace and Electronic Systems 43 (4) : pp. 1523 – 1543 Search in Google Scholar

Mahler, R. P. S. 2003. Multitarget bayes filtering via first-order multitarget moments. IEEE Transactions on Aerospace and Electronic Systems 39(4): 1152–1178. Mahler R. P. S. 2003 Multitarget bayes filtering via first-order multitarget moments IEEE Transactions on Aerospace and Electronic Systems 39 (4) 1152 – 1178 Search in Google Scholar

Mahler, R. P. S. 2007. Statistical Multisource-Multitarget Information Fusion Artech House, Inc. Mahler R. P. S. 2007 Statistical Multisource-Multitarget Information Fusion Artech House, Inc Search in Google Scholar

Privault, N. and Teoh, T. 2019. Second order multi-object filtering with target interaction using determinantal point processes. Tech. Rep. arXiv:1906.06522 [math.PR], ArXiV, June. Privault N. and Teoh T. 2019 Second order multi-object filtering with target interaction using determinantal point processes Tech. Rep. arXiv:1906.06522 [math.PR], ArXiV, June Search in Google Scholar

Reuter, S., Wilking, B., Wiest, J., Munz, M. and Dietmayer, K. 2013. Real-time multi-object tracking using random finite sets. IEEE Transactions on Aerospace and Electronic Systems 49(4): 2666–2678. Reuter S. Wilking B. Wiest J. Munz M. and Dietmayer K. 2013 Real-time multi-object tracking using random finite sets IEEE Transactions on Aerospace and Electronic Systems 49 (4) 2666 – 2678 Search in Google Scholar

Ristic, B. 2013. Multi-Object Particle Filters Springer New York, New York, NY, pp. 53–84. Ristic B. 2013 Multi-Object Particle Filters Springer New York New York, NY , pp. 53 – 84 Search in Google Scholar

Ristic, B., Vo, B. T., Vo, B. N. and Farina, A. 2013. A tutorial on bernoulli filters: Theory, implementation and applications. IEEE Transactions on Signal Processing 61 July, pp. 3406–3430. Ristic B. Vo B. T. Vo B. N. and Farina A. 2013 A tutorial on bernoulli filters: Theory, implementation and applications IEEE Transactions on Signal Processing 61 July , pp. 3406 – 3430 Search in Google Scholar

Solis Montero, A., Lang, J. and Laganiere, R. 2015. Scalable kernel correlation filter with sparse feature integration. 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, December, pp. 587–594. Solis Montero A. Lang J. and Laganiere R. 2015 Scalable kernel correlation filter with sparse feature integration 2015 IEEE International Conference on Computer Vision (ICCV), Santiago Chile December , pp. 587 – 594 Search in Google Scholar

Vo, B.-N., Vo, B.-T., Pham, N.-T. and Suter, D. 2010. Joint detection and estimation of multiple objects from image observations. IEEE Transactions on Signal Processing 58(10): 5129–5141. Vo B.-N. Vo B.-T. Pham N.-T. and Suter D. 2010 Joint detection and estimation of multiple objects from image observations IEEE Transactions on Signal Processing 58 (10) 5129 – 5141 Search in Google Scholar

Wang, N., Shi, J., Yeung, D.-Y. and Jia, J. 2015. Understanding and diagnosing visual tracking systems. The IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, December, pp. 3101–3109, doi: 10.1109/ICCV.2015.355. Wang N. Shi J. Yeung D.-Y. and Jia J. 2015 Understanding and diagnosing visual tracking systems The IEEE International Conference on Computer Vision (ICCV), Santiago Chile December , pp. 3101 – 3109 , doi: 10.1109/ICCV.2015.355 Open DOISearch in Google Scholar

Xu, T., Feng, Z.-H., Wu, X.-J. and Kittler, J. 2019. Learning adaptive discriminative correlation filters via temporal consistency preserving spatial feature selection for robust visual object tracking. IEEE Transactions on Image Processing 28(11): 5596–5609. Xu T. Feng Z.-H. Wu X.-J. and Kittler J. 2019 Learning adaptive discriminative correlation filters via temporal consistency preserving spatial feature selection for robust visual object tracking IEEE Transactions on Image Processing 28(11 ): 5596 – 5609 Search in Google Scholar