Patino, L., Hubner, M., King, R., Litzenberger, M., Roupioz, L., Michon, K., Szklarski, L., Pegoraro, J., Stoianov, N. and Ferryman, J. (2022) Fusion of heterogenous sensor data in border surveillance. Sensors, 22 (19). 7351. ISSN 1424-8220 doi: https://doi.org/10.3390/s22197351

Patino, L., Boyle, J. ORCID: https://orcid.org/0000-0002-5785-8046, Ferryman, J., Auer, J., Pegoraro, J., Pflugfelder, R., Cokbas, M., Konrad, J., Ishwar, P., Slavic, G., Marcenaro, L., Jiang, Y., Jin, Y., Ko, H., Zhao, G., Ben-Yosef, G. and Qiu, J. (2021) PETS2021: through-foliage detection and tracking challenge and evaluation. In: 2021 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 16-19 NOV 2021, Virtual. doi: https://doi.org/10.1109/AVSS52988.2021.9663837

Patino, L., Cane, T. and Ferryman, J. (2021) A comprehensive maritime benchmark dataset for detection, tracking and threat recognition. In: 17th IEEE Int'l Conf on Advanced Video and Signal-based Surveillance (AVSS 2021), 16-19 NOV 2021, Virtual. doi: https://doi.org/10.1109/AVSS52988.2021.9663739

Galdi, C., Boyle, J. ORCID: https://orcid.org/0000-0002-5785-8046, Chen, L., Chiesa, V., Debiasi, L., Dugelay, J.-L., Ferryman, J., Grudzien, A., Kauba, C., Kirchgasser, S., Kowalski, M., Linortner, M., Maik, P., Michon, K., Patino, L., Prommegger, B., Sequeira, A., Szklarski, Ł. and Uhl, A. (2020) PROTECT: pervasive and useR fOcused biomeTrics bordEr projeCT. A Case Study. IET Biometrics. ISSN 2047-4946 doi: https://doi.org/10.1049/iet-bmt.2020.0033

Patino, L., Nawaz, T., Cane, T. and Ferryman, J. (2017) PETS 2017: dataset and challenge. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 26 July 2017, Honolulu, HI, USA. doi: https://doi.org/10.1109/CVPRW.2017.264

Patino, L. and Ferryman, J. (2017) Loitering behaviour detection of boats at sea. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 26 July 2017, Honolulu, HI, USA.

Patino, L., Cane, T., Vallee, A. and Ferryman, J. (2016) PETS 2016: dataset and challenge. In: IEEE Cnference on Computer Vision and Pattern Recognition Workshops (CVPRW), 26 June-1 July 2016, Las Vegas, USA, pp. 1240-1247.

Patino, L. and Ferryman, J. (2016) Semantic modelling for behaviour characterisation and threat detection. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 26 June-1 July 2016, Las Vegas, USA, pp. 1282-1288.

Patino, L. and Ferryman, J. (2016) Detecting threat behaviours. In: 13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), August 23-26, 2016, Colorado Springs, US, pp. 88-94.

Andersson, M., Johansson, R., Stenborg, K.-G., Forsgren, R., Cane, T., Taberski, G., Patino, L. and Ferryman, J. (2016) The IPATCH system for maritime surveillance and piracy threat classification. In: 2016 European Intelligence and Security Informatics Conference (EISIC), 17-19 Aug 2016, Uppsala, Sweden, p. 200. doi: https://doi.org/10.1109/EISIC.2016.054

Patino, L. and Ferryman, J. (2015) PETS 2014: dataset and challenge. In: 11th IEEE International Conference on Advanced Video- and Signal-based Surveillance (AVSS 2014), August 26-29, 2014, Seoul, Korea, pp. 1-6.

Patino, L. and Ferryman, J. (2015) Meeting detection in video through semantic analysis. In: 12th IEEE International Conference on Advanced Video- and Signal-based Surveillance (AVSS2015), August 25-28, 2015, Karlsruhe, Germany, pp. 1-6.

Patino, L. and Ferryman, J. (2014) Multicamera trajectory analysis for semantic behaviour characterisation. In: 11th IEEE International Conference on Advanced Video- and Signal-based Surveillance (AVSS2014), August 26-29, 2014, Seoul, Korea, pp. 1-6.

Sanromà, G., Patino, L., Burghouts, G., Schutte, K. and Ferryman, J. (2014) A unified approach to the recognition of complex actions from sequences of zone-crossings. Image and Vision Computing, 32 (5). pp. 363-378. ISSN 0262-8856 doi: https://doi.org/10.1016/j.imavis.2014.02.005

Patino, L. and Ferryman, J. (2014) Multiresolution semantic activity characterisation and abnormality discovery in videos. Applied Soft Computing, 25. pp. 485-495. ISSN 15684946 doi: https://doi.org/10.1016/j.asoc.2014.08.039