{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:49:34Z","timestamp":1742914174927,"version":"3.40.3"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030368074"},{"type":"electronic","value":"9783030368081"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-36808-1_50","type":"book-chapter","created":{"date-parts":[[2019,12,6]],"date-time":"2019-12-06T15:04:08Z","timestamp":1575644648000},"page":"458-465","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep Learning and Statistical Models for Time-Critical Pedestrian Behaviour Prediction"],"prefix":"10.1007","author":[{"given":"Joel Janek","family":"Dabrowski","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Johan Pieter","family":"de Villiers","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ashfaqur","family":"Rahman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Conrad","family":"Beyers","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,12,5]]},"reference":[{"key":"50_CR1","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9780511804779","volume-title":"Bayesian Reasoning and Machine Learning","author":"D Barber","year":"2012","unstructured":"Barber, D.: Bayesian Reasoning and Machine Learning. Cambridge University Press, Cambridge (2012)"},{"issue":"16","key":"50_CR2","doi-asserted-by":"publisher","first-page":"1468","DOI":"10.1049\/joe.2018.8316","volume":"2018","author":"B Cheng","year":"2018","unstructured":"Cheng, B., Xu, X., Zeng, Y., Ren, J., Jung, S.: Pedestrian trajectory prediction via the social-grid LSTM model. J. Eng. 2018(16), 1468\u20131474 (2018). https:\/\/doi.org\/10.1049\/joe.2018.8316","journal-title":"J. Eng."},{"key":"50_CR3","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1016\/j.eswa.2016.06.024","volume":"62","author":"JJ Dabrowski","year":"2016","unstructured":"Dabrowski, J.J., Beyers, C., de Villiers, J.P.: Systemic banking crisis early warning systems using dynamic Bayesian networks. Expert Syst. Appl. 62, 225\u2013242 (2016). https:\/\/doi.org\/10.1016\/j.eswa.2016.06.024","journal-title":"Expert Syst. Appl."},{"key":"50_CR4","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1016\/j.inffus.2014.07.001","volume":"23","author":"JJ Dabrowski","year":"2015","unstructured":"Dabrowski, J.J., de Villiers, J.P.: Maritime piracy situation modelling with dynamic Bayesian networks. Inf. Fusion 23, 116\u2013130 (2015). https:\/\/doi.org\/10.1016\/j.inffus.2014.07.001","journal-title":"Inf. Fusion"},{"issue":"19","key":"50_CR5","doi-asserted-by":"publisher","first-page":"6738","DOI":"10.1016\/j.eswa.2015.04.061","volume":"42","author":"JJ Dabrowski","year":"2015","unstructured":"Dabrowski, J.J., de Villiers, J.P.: A unified model for context-based behavioural modelling and classification. Expert Syst. Appl. 42(19), 6738\u20136757 (2015). https:\/\/doi.org\/10.1016\/j.eswa.2015.04.061","journal-title":"Expert Syst. Appl."},{"key":"50_CR6","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1016\/j.engappai.2017.06.005","volume":"64","author":"JJ Dabrowski","year":"2017","unstructured":"Dabrowski, J.J., de Villiers, J.P., Beyers, C.: Context-based behaviour modelling and classification of marine vessels in an abalone poaching situation. Eng. Appl. Artif. Intell. 64, 95\u2013111 (2017). https:\/\/doi.org\/10.1016\/j.engappai.2017.06.005","journal-title":"Eng. Appl. Artif. Intell."},{"key":"50_CR7","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1016\/j.inffus.2017.10.002","volume":"42","author":"JJ Dabrowski","year":"2018","unstructured":"Dabrowski, J.J., de Villiers, J.P., Beyers, C.: Naive Bayes switching linear dynamical system: a model for dynamic system modelling, classification, and information fusion. Inf. Fusion 42, 75\u2013101 (2018). https:\/\/doi.org\/10.1016\/j.inffus.2017.10.002","journal-title":"Inf. Fusion"},{"issue":"8","key":"50_CR8","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997). https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735","journal-title":"Neural Comput."},{"key":"50_CR9","doi-asserted-by":"publisher","unstructured":"Hoy, M., Tu, Z., Dang, K., Dauwels, J.: Learning to predict pedestrian intention via variational tracking networks. In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pp. 3132\u20133137, November 2018. https:\/\/doi.org\/10.1109\/ITSC.2018.8569641","DOI":"10.1109\/ITSC.2018.8569641"},{"key":"50_CR10","doi-asserted-by":"publisher","unstructured":"Hug, R., Becker, S., Hbner, W., Arens, M.: Particle-based pedestrian path prediction using LSTM-MDL models. In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pp. 2684\u20132691, November 2018. https:\/\/doi.org\/10.1109\/ITSC.2018.8569478","DOI":"10.1109\/ITSC.2018.8569478"},{"key":"50_CR11","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint: arXiv:1412.6980 (2014)"},{"issue":"2","key":"50_CR12","doi-asserted-by":"publisher","first-page":"322","DOI":"10.1109\/TPAMI.2015.2443801","volume":"38","author":"JFP Kooij","year":"2016","unstructured":"Kooij, J.F.P., Englebienne, G., Gavrila, D.M.: Mixture of switching linear dynamics to discover behavior patterns in object tracks. IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 322\u2013334 (2016). https:\/\/doi.org\/10.1109\/TPAMI.2015.2443801","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"50_CR13","doi-asserted-by":"publisher","unstructured":"Kooij, J.F.P., Schneider, N., Gavrila, D.M.: Analysis of pedestrian dynamics from a vehicle perspective. In: 2014 IEEE Intelligent Vehicles Symposium Proceedings, pp. 1445\u20131450, June 2014. https:\/\/doi.org\/10.1109\/IVS.2014.6856505","DOI":"10.1109\/IVS.2014.6856505"},{"key":"50_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"618","DOI":"10.1007\/978-3-319-10599-4_40","volume-title":"Computer Vision \u2013 ECCV 2014","author":"JFP Kooij","year":"2014","unstructured":"Kooij, J.F.P., Schneider, N., Flohr, F., Gavrila, D.M.: Context-based pedestrian path prediction. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part VI. LNCS, vol. 8694, pp. 618\u2013633. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10599-4_40"},{"issue":"4","key":"50_CR15","doi-asserted-by":"publisher","first-page":"985","DOI":"10.1109\/TMM.2017.2759508","volume":"20","author":"J Li","year":"2018","unstructured":"Li, J., Liang, X., Shen, S., Xu, T., Feng, J., Yan, S.: Scale-aware fast R-CNN for pedestrian detection. IEEE Trans. Multimed. 20(4), 985\u2013996 (2018). https:\/\/doi.org\/10.1109\/TMM.2017.2759508","journal-title":"IEEE Trans. Multimed."},{"issue":"5","key":"50_CR16","doi-asserted-by":"publisher","first-page":"1803","DOI":"10.1109\/TITS.2018.2836305","volume":"20","author":"Raul Quintero Minguez","year":"2019","unstructured":"Minguez, R.Q., Alonso, I.P., Fernandez-Llorca, D., Sotelo, M.A.: Pedestrian path, pose, and intention prediction through Gaussian process dynamical models and pedestrian activity recognition. IEEE Trans. Intell. Transp. Syst., 1\u201312 (2018). https:\/\/doi.org\/10.1109\/TITS.2018.2836305","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"50_CR17","unstructured":"Murphy, K.P.: Switching Kalman filters. Technical report, Department of Computer Science, UC Berkeley (1998)"},{"key":"50_CR18","doi-asserted-by":"publisher","unstructured":"Ridel, D., Rehder, E., Lauer, M., Stiller, C., Wolf, D.: A literature review on the prediction of pedestrian behavior in urban scenarios. In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pp. 3105\u20133112, November 2018. https:\/\/doi.org\/10.1109\/ITSC.2018.8569415","DOI":"10.1109\/ITSC.2018.8569415"},{"issue":"4","key":"50_CR19","doi-asserted-by":"publisher","first-page":"414","DOI":"10.1109\/TIV.2018.2873901","volume":"3","author":"K Saleh","year":"2018","unstructured":"Saleh, K., Hossny, M., Nahavandi, S.: Intent prediction of pedestrians via motion trajectories using stacked recurrent neural networks. IEEE Trans. Intell. Veh. 3(4), 414\u2013424 (2018). https:\/\/doi.org\/10.1109\/TIV.2018.2873901","journal-title":"IEEE Trans. Intell. Veh."},{"key":"50_CR20","doi-asserted-by":"publisher","unstructured":"Saleh, K., Hossny, M., Nahavandi, S.: Long-term recurrent predictive model for intent prediction of pedestrians via inverse reinforcement learning. In: 2018 Digital Image Computing: Techniques and Applications (DICTA), pp. 1\u20138, December 2018. https:\/\/doi.org\/10.1109\/DICTA.2018.8615854","DOI":"10.1109\/DICTA.2018.8615854"},{"key":"50_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1007\/978-3-642-40602-7_18","volume-title":"Pattern Recognition","author":"N Schneider","year":"2013","unstructured":"Schneider, N., Gavrila, D.M.: Pedestrian path prediction with recursive Bayesian filters: a comparative study. In: Weickert, J., Hein, M., Schiele, B. (eds.) GCPR 2013. LNCS, vol. 8142, pp. 174\u2013183. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-40602-7_18"},{"key":"50_CR22","doi-asserted-by":"publisher","unstructured":"Schulz, A.T., Stiefelhagen, R.: Pedestrian intention recognition using latent-dynamic conditional random fields. In: 2015 IEEE Intelligent Vehicles Symposium (IV), pp. 622\u2013627, June 2015. https:\/\/doi.org\/10.1109\/IVS.2015.7225754","DOI":"10.1109\/IVS.2015.7225754"},{"key":"50_CR23","doi-asserted-by":"publisher","unstructured":"Volz, B., Behrendt, K., Mielenz, H., Gilitschenski, I., Siegwart, R., Nieto, J.: A data-driven approach for pedestrian intention estimation. In: 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), pp. 2607\u20132612, November 2016. https:\/\/doi.org\/10.1109\/ITSC.2016.7795975","DOI":"10.1109\/ITSC.2016.7795975"}],"container-title":["Communications in Computer and Information Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-36808-1_50","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T13:25:44Z","timestamp":1710249944000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-36808-1_50"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030368074","9783030368081"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-36808-1_50","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"5 December 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Sydney, NSW","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 December 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 December 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/ajiips.com.au\/iconip2019\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}