Human trajectory prediction and generation using LSTM models and GANs
•New deep neural network models are proposed for trajectory prediction.•LSTM and GAN1 models are used for unimodal predictions, GAN3 model for multimodal.•Metrics are proposed for normalizing errors for more consistent comparisons.•New dataset are proposed with low linearity and a high diversity. Hu...
Saved in:
| Published in: | Pattern recognition Vol. 120; p. 108136 |
|---|---|
| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier Ltd
01.12.2021
|
| Subjects: | |
| ISSN: | 0031-3203, 1873-5142 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | •New deep neural network models are proposed for trajectory prediction.•LSTM and GAN1 models are used for unimodal predictions, GAN3 model for multimodal.•Metrics are proposed for normalizing errors for more consistent comparisons.•New dataset are proposed with low linearity and a high diversity.
Human trajectory prediction is an important topic in several application domains, ranging from self-driving cars to environment design and planning, from socially-aware robots to intelligent tracking systems. This complex subject comes with different challenges, such as human-space interaction, human-human interaction, multimodality, and generalizability. Currently, these challenges, especially generalizability, have not been completely explored by state-of-the-art works. This work attempts to fill this gap by proposing and defining new methods and metrics to help understand trajectories. In particular, new deep learning models based on Long Short-Term Memory and Generative Adversarial Network architectures are used in both unimodal and multimodal contexts. These approaches are evaluated with new error metrics, which normalize some biases in standard metrics. Tests have been assessed using newly collected datasets characterized by a higher diversity and lower linearity than those used in state-of-the-art works. The results prove that the proposed models and datasets are comparable to and yield better generalizability than state-of-the-art works. Moreover, we also prove that our datasets better represent multimodal scenarios (allowing for multiple possible behaviors) and that human trajectories are moderately influenced by their spatial region and slightly influenced by their date and time. |
|---|---|
| AbstractList | •New deep neural network models are proposed for trajectory prediction.•LSTM and GAN1 models are used for unimodal predictions, GAN3 model for multimodal.•Metrics are proposed for normalizing errors for more consistent comparisons.•New dataset are proposed with low linearity and a high diversity.
Human trajectory prediction is an important topic in several application domains, ranging from self-driving cars to environment design and planning, from socially-aware robots to intelligent tracking systems. This complex subject comes with different challenges, such as human-space interaction, human-human interaction, multimodality, and generalizability. Currently, these challenges, especially generalizability, have not been completely explored by state-of-the-art works. This work attempts to fill this gap by proposing and defining new methods and metrics to help understand trajectories. In particular, new deep learning models based on Long Short-Term Memory and Generative Adversarial Network architectures are used in both unimodal and multimodal contexts. These approaches are evaluated with new error metrics, which normalize some biases in standard metrics. Tests have been assessed using newly collected datasets characterized by a higher diversity and lower linearity than those used in state-of-the-art works. The results prove that the proposed models and datasets are comparable to and yield better generalizability than state-of-the-art works. Moreover, we also prove that our datasets better represent multimodal scenarios (allowing for multiple possible behaviors) and that human trajectories are moderately influenced by their spatial region and slightly influenced by their date and time. |
| ArticleNumber | 108136 |
| Author | Pierdicca, Roberto Paolanti, Marina Frontoni, Emanuele Rossi, Luca |
| Author_xml | – sequence: 1 givenname: Luca surname: Rossi fullname: Rossi, Luca organization: Università Politecnica delle Marche, Dipartimento di Ingegneria dell’Informazione, Ancona 60100, Italy – sequence: 2 givenname: Marina surname: Paolanti fullname: Paolanti, Marina email: m.paolanti@staff.univpm.it organization: Università Politecnica delle Marche, Dipartimento di Ingegneria dell’Informazione, Ancona 60100, Italy – sequence: 3 givenname: Roberto surname: Pierdicca fullname: Pierdicca, Roberto organization: Università Politecnica delle Marche, Dipartimento di Ingegneria Civile, Edile e dell’Architettura, Ancona 60100, Italy – sequence: 4 givenname: Emanuele surname: Frontoni fullname: Frontoni, Emanuele organization: Università Politecnica delle Marche, Dipartimento di Ingegneria dell’Informazione, Ancona 60100, Italy |
| BookMark | eNqFkMtOwzAQRS1UJNrCH7DID6T4kTgJC6SqKi1SgQVlbTn2uHLUOpWdIvXvcRNWLGA1mscZ6Z4JGrnWAUL3BM8IJvyhmR1lp9rdjGJK4qgkjF-hMSkLluYkoyM0xpiRlFHMbtAkhAZjUsTFGC3Xp4N0SedlA6pr_Tk5etBWdbZ1iXQ62YEDL_v2FKzbJZuP7WtyaDXsQ3-wmr-FW3Rt5D7A3U-dos_n5XaxTjfvq5fFfJMqltMu5abUBiQnBitaGZ3nVVHzqiyxymVOqwJqqSpjNAEscW0AdM0JyTjU1CidsSnKhr_KtyF4MOLo7UH6syBYXFSIRgwqxEWFGFRE7PEXpmzXZ4q57f4_-GmAY2D4suBFUBacipZ8VCZ0a_9-8A2lG3-y |
| CitedBy_id | crossref_primary_10_1109_JIOT_2022_3203415 crossref_primary_10_1016_j_patcog_2025_112315 crossref_primary_10_1007_s10489_022_04093_z crossref_primary_10_1016_j_patcog_2022_108894 crossref_primary_10_1016_j_cviu_2024_104142 crossref_primary_10_1016_j_patcog_2021_108252 crossref_primary_10_1016_j_trc_2023_104304 crossref_primary_10_1016_j_knosys_2025_113019 crossref_primary_10_3390_s23156997 crossref_primary_10_1002_int_22900 crossref_primary_10_1007_s11042_024_18228_6 crossref_primary_10_1016_j_neucom_2022_07_085 crossref_primary_10_1016_j_patcog_2025_112270 crossref_primary_10_3390_s23041939 crossref_primary_10_3389_fnbot_2024_1284175 crossref_primary_10_1109_ACCESS_2025_3566337 crossref_primary_10_1016_j_cosrev_2024_100635 crossref_primary_10_1109_TNSE_2022_3140529 crossref_primary_10_1038_s41598_025_86785_3 crossref_primary_10_1080_15568318_2025_2510413 crossref_primary_10_1016_j_patcog_2022_109234 crossref_primary_10_3390_app14209349 crossref_primary_10_1007_s11600_022_00831_6 crossref_primary_10_3390_ijgi13030072 crossref_primary_10_1016_j_pmcj_2023_101809 crossref_primary_10_3390_electronics14122480 crossref_primary_10_1109_ACCESS_2024_3426958 crossref_primary_10_1016_j_ins_2024_120455 crossref_primary_10_1016_j_trc_2025_105145 crossref_primary_10_1016_j_knosys_2023_110637 crossref_primary_10_1016_j_robot_2022_104352 crossref_primary_10_1016_j_patcog_2022_108552 crossref_primary_10_3390_vehicles7020057 crossref_primary_10_3390_su15043535 crossref_primary_10_1016_j_neucom_2023_127117 crossref_primary_10_1145_3716637 crossref_primary_10_1109_TITS_2023_3252262 crossref_primary_10_1016_j_daach_2024_e00340 crossref_primary_10_1016_j_eswa_2023_122423 crossref_primary_10_1016_j_rcim_2024_102864 crossref_primary_10_1080_13658816_2024_2436482 crossref_primary_10_1016_j_ipm_2024_103954 crossref_primary_10_2478_amns_2023_1_00481 crossref_primary_10_1016_j_jag_2023_103412 crossref_primary_10_1016_j_patcog_2023_109997 crossref_primary_10_1016_j_eswa_2024_124635 crossref_primary_10_1016_j_patcog_2023_109633 crossref_primary_10_1109_ACCESS_2024_3428438 crossref_primary_10_3390_app11188299 crossref_primary_10_1061_JCEMD4_COENG_15888 crossref_primary_10_1016_j_neucom_2022_09_005 crossref_primary_10_1016_j_patcog_2023_109559 crossref_primary_10_1186_s40537_023_00727_2 |
| Cites_doi | 10.1109/CVPR42600.2020.01052 10.1016/j.eswa.2018.03.035 10.1016/j.patcog.2019.04.025 10.1007/s00138-020-01118-w 10.1109/TPAMI.2017.2728788 10.1016/j.eswa.2019.01.027 10.1162/neco.1997.9.8.1735 10.1007/s10846-017-0674-7 10.1016/j.jretconser.2018.11.005 10.1016/j.eswa.2019.06.041 |
| ContentType | Journal Article |
| Copyright | 2021 |
| Copyright_xml | – notice: 2021 |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.patcog.2021.108136 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1873-5142 |
| ExternalDocumentID | 10_1016_j_patcog_2021_108136 S003132032100323X |
| GroupedDBID | --K --M -D8 -DT -~X .DC .~1 0R~ 123 1B1 1RT 1~. 1~5 29O 4.4 457 4G. 53G 5VS 7-5 71M 8P~ 9JN AABNK AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AAXUO AAYFN ABBOA ABEFU ABFNM ABFRF ABHFT ABJNI ABMAC ABTAH ABXDB ABYKQ ACBEA ACDAQ ACGFO ACGFS ACNNM ACRLP ACZNC ADBBV ADEZE ADJOM ADMUD ADMXK ADTZH AEBSH AECPX AEFWE AEKER AENEX AFKWA AFTJW AGHFR AGUBO AGYEJ AHHHB AHJVU AHZHX AIALX AIEXJ AIKHN AITUG AJBFU AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD ASPBG AVWKF AXJTR AZFZN BJAXD BKOJK BLXMC CS3 DU5 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 F0J F5P FD6 FDB FEDTE FGOYB FIRID FNPLU FYGXN G-Q G8K GBLVA GBOLZ HLZ HVGLF HZ~ H~9 IHE J1W JJJVA KOM KZ1 LG9 LMP LY1 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 R2- RIG RNS ROL RPZ SBC SDF SDG SDP SDS SES SEW SPC SPCBC SST SSV SSZ T5K TN5 UNMZH VOH WUQ XJE XPP ZMT ZY4 ~G- 9DU AATTM AAXKI AAYWO AAYXX ABDPE ABWVN ACLOT ACRPL ACVFH ADCNI ADNMO AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP CITATION EFKBS ~HD |
| ID | FETCH-LOGICAL-c352t-6f8dfea61f0c29fd5597b69880c5a5297ebac9ffd1e0a0bfeedb61146eb2fcd43 |
| ISICitedReferencesCount | 64 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000691531800018&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0031-3203 |
| IngestDate | Sat Nov 29 07:25:29 EST 2025 Tue Nov 18 21:44:55 EST 2025 Fri Feb 23 02:42:55 EST 2024 |
| IsDoiOpenAccess | false |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Trajectory prediction Trajectory generation LSTM GANs |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c352t-6f8dfea61f0c29fd5597b69880c5a5297ebac9ffd1e0a0bfeedb61146eb2fcd43 |
| OpenAccessLink | https://www.ncbi.nlm.nih.gov/pmc/articles/8248611 |
| ParticipantIDs | crossref_primary_10_1016_j_patcog_2021_108136 crossref_citationtrail_10_1016_j_patcog_2021_108136 elsevier_sciencedirect_doi_10_1016_j_patcog_2021_108136 |
| PublicationCentury | 2000 |
| PublicationDate | December 2021 2021-12-00 |
| PublicationDateYYYYMMDD | 2021-12-01 |
| PublicationDate_xml | – month: 12 year: 2021 text: December 2021 |
| PublicationDecade | 2020 |
| PublicationTitle | Pattern recognition |
| PublicationYear | 2021 |
| Publisher | Elsevier Ltd |
| Publisher_xml | – name: Elsevier Ltd |
| References | Helbing, Molnar (bib0005) 1998; 51 Lerner, Chrysanthou, Lischinski (bib0027) 2007; 26 Hochreiter, Schmidhuber (bib0001) 1997; 9 Ferracuti, Norscini, Frontoni, Gabellini, Paolanti, Placidi (bib0012) 2019; 47 Bermingham, Lee (bib0011) 2019; 122 Bartoli, Lisanti, Ballan, Del Bimbo (bib0020) 2018 Magdy, Sakr, Mostafa, El-Bahnasy (bib0030) 2015 Kitani, Ziebart, Bagnell, Hebert (bib0014) 2012 Sadeghian, Kosaraju, Sadeghian, Hirose, Rezatofighi, Savarese (bib0019) 2019 Paolanti, Liciotti, Pietrini, Mancini, Frontoni (bib0013) 2018; 91 N. Jaipuria, G. Habibi, J.P. How, A transferable pedestrian motion prediction model for intersections with different geometries Lee, Choi, Vernaza, Choy, Torr, Chandraker (bib0022) 2017 Gupta, Johnson, Fei-Fei, Savarese, Alahi (bib0007) 2018 Pellegrini, Ess, Schindler, Van Gool (bib0026) 2009 Afsar, Cortez, Santos (bib0009) 2018; 110 Mehran, Oyama, Shah (bib0004) 2009 Chen, Cai, Zhang, Wu, Mu, Li, Sotelo (bib0010) 2019; 138 Goodfellow, Pouget-Abadie, Mirza, Xu, Warde-Farley, Ozair, Courville, Bengio (bib0002) 2014 Ballan, Castaldo, Alahi, Palmieri, Savarese (bib0015) 2016 H. Manh, G. Alaghband, Scene-LSTM: a model for human trajectory prediction Srivastava, Mansimov, Salakhudinov (bib0023) 2015 Y. Chai, B. Sapp, M. Bansal, D. Anguelov, Multipath: multiple probabilistic anchor trajectory hypotheses for behavior prediction, 2019. Paolanti, Pietrini, Mancini, Frontoni, Zingaretti (bib0029) 2020; 31 Pei, Qi, Zhang, Ma, Yang (bib0021) 2019; 93 Luber, Stork, Tipaldi, Arras (bib0003) 2010 (2018). J. Liang, L. Jiang, K. Murphy, T. Yu, A. Hauptmann, The garden of forking paths: towards multi-future trajectory prediction, 2019. Xie, Shu, Todorovic, Zhu (bib0016) 2017; 40 Liang, Jiang, Niebles, Hauptmann, Fei-Fei (bib0008) 2019 Alahi, Goel, Ramanathan, Robicquet, Fei-Fei, Savarese (bib0006) 2016 Gabellini, D’Aloisio, Fabiani, Placidi (bib0028) 2019 Chen (10.1016/j.patcog.2021.108136_bib0010) 2019; 138 Bartoli (10.1016/j.patcog.2021.108136_bib0020) 2018 Luber (10.1016/j.patcog.2021.108136_bib0003) 2010 Ballan (10.1016/j.patcog.2021.108136_bib0015) 2016 Mehran (10.1016/j.patcog.2021.108136_bib0004) 2009 Lee (10.1016/j.patcog.2021.108136_bib0022) 2017 Gupta (10.1016/j.patcog.2021.108136_bib0007) 2018 Alahi (10.1016/j.patcog.2021.108136_bib0006) 2016 10.1016/j.patcog.2021.108136_bib0025 10.1016/j.patcog.2021.108136_bib0024 Liang (10.1016/j.patcog.2021.108136_bib0008) 2019 Paolanti (10.1016/j.patcog.2021.108136_bib0013) 2018; 91 Srivastava (10.1016/j.patcog.2021.108136_bib0023) 2015 Sadeghian (10.1016/j.patcog.2021.108136_bib0019) 2019 Goodfellow (10.1016/j.patcog.2021.108136_bib0002) 2014 Xie (10.1016/j.patcog.2021.108136_bib0016) 2017; 40 Pellegrini (10.1016/j.patcog.2021.108136_bib0026) 2009 Paolanti (10.1016/j.patcog.2021.108136_bib0029) 2020; 31 Bermingham (10.1016/j.patcog.2021.108136_bib0011) 2019; 122 Afsar (10.1016/j.patcog.2021.108136_bib0009) 2018; 110 Pei (10.1016/j.patcog.2021.108136_bib0021) 2019; 93 Ferracuti (10.1016/j.patcog.2021.108136_bib0012) 2019; 47 Gabellini (10.1016/j.patcog.2021.108136_bib0028) 2019 Kitani (10.1016/j.patcog.2021.108136_bib0014) 2012 Hochreiter (10.1016/j.patcog.2021.108136_bib0001) 1997; 9 Helbing (10.1016/j.patcog.2021.108136_bib0005) 1998; 51 10.1016/j.patcog.2021.108136_bib0018 10.1016/j.patcog.2021.108136_bib0017 Lerner (10.1016/j.patcog.2021.108136_bib0027) 2007; 26 Magdy (10.1016/j.patcog.2021.108136_bib0030) 2015 |
| References_xml | – start-page: 613 year: 2015 end-page: 619 ident: bib0030 article-title: Review on trajectory similarity measures publication-title: 2015 IEEE seventh international conference on Intelligent Computing and Information Systems (ICICIS) – reference: (2018). – volume: 93 start-page: 273 year: 2019 end-page: 282 ident: bib0021 article-title: Human trajectory prediction in crowded scene using social-affinity long short-term memory publication-title: Pattern Recognit. – volume: 138 start-page: 112753 year: 2019 ident: bib0010 article-title: A novel sparse representation model for pedestrian abnormal trajectory understanding publication-title: Expert Syst. Appl. – volume: 110 start-page: 41 year: 2018 end-page: 51 ident: bib0009 article-title: Automatic human trajectory destination prediction from video publication-title: Expert Syst. Appl. – volume: 26 start-page: 655 year: 2007 end-page: 664 ident: bib0027 article-title: Crowds by example publication-title: Computer Graphics Forum – volume: 122 start-page: 334 year: 2019 end-page: 350 ident: bib0011 article-title: Mining place-matching patterns from spatio-temporal trajectories using complex real-world places publication-title: Expert Syst. Appl. – start-page: 201 year: 2012 end-page: 214 ident: bib0014 article-title: Activity forecasting publication-title: European Conference on Computer Vision – volume: 9 start-page: 1735 year: 1997 end-page: 1780 ident: bib0001 article-title: Long short-term memory publication-title: Neural Comput. – start-page: 336 year: 2017 end-page: 345 ident: bib0022 article-title: Desire: distant future prediction in dynamic scenes with interacting agents publication-title: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition – start-page: 1349 year: 2019 end-page: 1358 ident: bib0019 article-title: Sophie: an attentive GAN for predicting paths compliant to social and physical constraints publication-title: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition – start-page: 935 year: 2009 end-page: 942 ident: bib0004 article-title: Abnormal crowd behavior detection using social force model publication-title: 2009 IEEE Conference on Computer Vision and Pattern Recognition – start-page: 464 year: 2010 end-page: 469 ident: bib0003 article-title: People tracking with human motion predictions from social forces publication-title: 2010 IEEE International Conference on Robotics and Automation – reference: J. Liang, L. Jiang, K. Murphy, T. Yu, A. Hauptmann, The garden of forking paths: towards multi-future trajectory prediction, 2019. – reference: Y. Chai, B. Sapp, M. Bansal, D. Anguelov, Multipath: multiple probabilistic anchor trajectory hypotheses for behavior prediction, 2019. – reference: N. Jaipuria, G. Habibi, J.P. How, A transferable pedestrian motion prediction model for intersections with different geometries, – volume: 47 start-page: 184 year: 2019 end-page: 194 ident: bib0012 article-title: A business application of rtls technology in intelligent retail environment: defining the shopper’s preferred path and its segmentation publication-title: J. Retail. Consum. Serv. – start-page: 2672 year: 2014 end-page: 2680 ident: bib0002 article-title: Generative adversarial nets publication-title: Advances in Neural Information Processing Systems – start-page: 261 year: 2009 end-page: 268 ident: bib0026 article-title: You’ll never walk alone: modeling social behavior for multi-target tracking publication-title: 2009 IEEE 12th International Conference on Computer Vision – start-page: 2255 year: 2018 end-page: 2264 ident: bib0007 article-title: Social GAN: socially acceptable trajectories with generative adversarial networks publication-title: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition – start-page: 697 year: 2016 end-page: 713 ident: bib0015 article-title: Knowledge transfer for scene-specific motion prediction publication-title: European Conference on Computer Vision – volume: 40 start-page: 1639 year: 2017 end-page: 1652 ident: bib0016 article-title: Learning and inferring “dark matter” and predicting human intents and trajectories in videos publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – start-page: 5725 year: 2019 end-page: 5734 ident: bib0008 article-title: Peeking into the future: predicting future person activities and locations in videos publication-title: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition – volume: 31 start-page: 66 year: 2020 ident: bib0029 article-title: Deep understanding of shopper behaviours and interactions using RGB-D vision publication-title: Mach. Vis. Appl. – start-page: 961 year: 2016 end-page: 971 ident: bib0006 article-title: Social LSTM: human trajectory prediction in crowded spaces publication-title: Proceedings of the IEEE Conference on Computer Vision and pattern Recognition – start-page: 1941 year: 2018 end-page: 1946 ident: bib0020 article-title: Context-aware trajectory prediction publication-title: 2018 24th International Conference on Pattern Recognition (ICPR) – reference: H. Manh, G. Alaghband, Scene-LSTM: a model for human trajectory prediction, – volume: 91 start-page: 165 year: 2018 end-page: 180 ident: bib0013 article-title: Modelling and forecasting customer navigation in intelligent retail environments publication-title: J. Intell. Robot. Syst. – volume: 51 year: 1998 ident: bib0005 article-title: Social force model for pedestrian dynamics publication-title: Phys. Rev. E – start-page: 285 year: 2019 end-page: 295 ident: bib0028 article-title: A large scale trajectory dataset for shopper behaviour understanding publication-title: International Conference on Image Analysis and Processing – start-page: 843 year: 2015 end-page: 852 ident: bib0023 article-title: Unsupervised learning of video representations using LSTMs publication-title: International conference on machine learning – ident: 10.1016/j.patcog.2021.108136_bib0024 doi: 10.1109/CVPR42600.2020.01052 – start-page: 336 year: 2017 ident: 10.1016/j.patcog.2021.108136_bib0022 article-title: Desire: distant future prediction in dynamic scenes with interacting agents – volume: 110 start-page: 41 year: 2018 ident: 10.1016/j.patcog.2021.108136_bib0009 article-title: Automatic human trajectory destination prediction from video publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2018.03.035 – start-page: 261 year: 2009 ident: 10.1016/j.patcog.2021.108136_bib0026 article-title: You’ll never walk alone: modeling social behavior for multi-target tracking – ident: 10.1016/j.patcog.2021.108136_bib0018 – start-page: 843 year: 2015 ident: 10.1016/j.patcog.2021.108136_bib0023 article-title: Unsupervised learning of video representations using LSTMs – start-page: 5725 year: 2019 ident: 10.1016/j.patcog.2021.108136_bib0008 article-title: Peeking into the future: predicting future person activities and locations in videos – ident: 10.1016/j.patcog.2021.108136_bib0025 – start-page: 1941 year: 2018 ident: 10.1016/j.patcog.2021.108136_bib0020 article-title: Context-aware trajectory prediction – start-page: 1349 year: 2019 ident: 10.1016/j.patcog.2021.108136_bib0019 article-title: Sophie: an attentive GAN for predicting paths compliant to social and physical constraints – start-page: 285 year: 2019 ident: 10.1016/j.patcog.2021.108136_bib0028 article-title: A large scale trajectory dataset for shopper behaviour understanding – start-page: 613 year: 2015 ident: 10.1016/j.patcog.2021.108136_bib0030 article-title: Review on trajectory similarity measures – start-page: 464 year: 2010 ident: 10.1016/j.patcog.2021.108136_bib0003 article-title: People tracking with human motion predictions from social forces – volume: 26 start-page: 655 year: 2007 ident: 10.1016/j.patcog.2021.108136_bib0027 article-title: Crowds by example – volume: 93 start-page: 273 year: 2019 ident: 10.1016/j.patcog.2021.108136_bib0021 article-title: Human trajectory prediction in crowded scene using social-affinity long short-term memory publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2019.04.025 – volume: 51 year: 1998 ident: 10.1016/j.patcog.2021.108136_bib0005 article-title: Social force model for pedestrian dynamics publication-title: Phys. Rev. E – volume: 31 start-page: 66 issue: 7 year: 2020 ident: 10.1016/j.patcog.2021.108136_bib0029 article-title: Deep understanding of shopper behaviours and interactions using RGB-D vision publication-title: Mach. Vis. Appl. doi: 10.1007/s00138-020-01118-w – start-page: 935 year: 2009 ident: 10.1016/j.patcog.2021.108136_bib0004 article-title: Abnormal crowd behavior detection using social force model – volume: 40 start-page: 1639 issue: 7 year: 2017 ident: 10.1016/j.patcog.2021.108136_bib0016 article-title: Learning and inferring “dark matter” and predicting human intents and trajectories in videos publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2017.2728788 – ident: 10.1016/j.patcog.2021.108136_bib0017 – start-page: 201 year: 2012 ident: 10.1016/j.patcog.2021.108136_bib0014 article-title: Activity forecasting – start-page: 961 year: 2016 ident: 10.1016/j.patcog.2021.108136_bib0006 article-title: Social LSTM: human trajectory prediction in crowded spaces – volume: 122 start-page: 334 year: 2019 ident: 10.1016/j.patcog.2021.108136_bib0011 article-title: Mining place-matching patterns from spatio-temporal trajectories using complex real-world places publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2019.01.027 – start-page: 2672 year: 2014 ident: 10.1016/j.patcog.2021.108136_bib0002 article-title: Generative adversarial nets – volume: 9 start-page: 1735 year: 1997 ident: 10.1016/j.patcog.2021.108136_bib0001 article-title: Long short-term memory publication-title: Neural Comput. doi: 10.1162/neco.1997.9.8.1735 – start-page: 2255 year: 2018 ident: 10.1016/j.patcog.2021.108136_bib0007 article-title: Social GAN: socially acceptable trajectories with generative adversarial networks – volume: 91 start-page: 165 issue: 2 year: 2018 ident: 10.1016/j.patcog.2021.108136_bib0013 article-title: Modelling and forecasting customer navigation in intelligent retail environments publication-title: J. Intell. Robot. Syst. doi: 10.1007/s10846-017-0674-7 – volume: 47 start-page: 184 year: 2019 ident: 10.1016/j.patcog.2021.108136_bib0012 article-title: A business application of rtls technology in intelligent retail environment: defining the shopper’s preferred path and its segmentation publication-title: J. Retail. Consum. Serv. doi: 10.1016/j.jretconser.2018.11.005 – start-page: 697 year: 2016 ident: 10.1016/j.patcog.2021.108136_bib0015 article-title: Knowledge transfer for scene-specific motion prediction – volume: 138 start-page: 112753 year: 2019 ident: 10.1016/j.patcog.2021.108136_bib0010 article-title: A novel sparse representation model for pedestrian abnormal trajectory understanding publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2019.06.041 |
| SSID | ssj0017142 |
| Score | 2.6207364 |
| Snippet | •New deep neural network models are proposed for trajectory prediction.•LSTM and GAN1 models are used for unimodal predictions, GAN3 model for... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 108136 |
| SubjectTerms | GANs LSTM Trajectory generation Trajectory prediction |
| Title | Human trajectory prediction and generation using LSTM models and GANs |
| URI | https://dx.doi.org/10.1016/j.patcog.2021.108136 |
| Volume | 120 |
| WOSCitedRecordID | wos000691531800018&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1873-5142 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017142 issn: 0031-3203 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Za9wwEBZt0oe-9C5NL_TQt6JgW7ZlPy5le5EsC9nCvhkdo5KleJc9SvLvMzpsb5PSC_oijG35mPk8mhnPQcgbJRIotalYYi2wPAPBZK4NK4zSQnEJBnyi8ImYTKr5vJ7GcNuNbycg2ra6uKhX_5XVuA-Z7VJn_4Ld_UVxB24j03FEtuP4R4wPbvntWi68Q_7SlQEw57EjeGtcz2SIbN95R8HJ2ew0dMQJ9Zo_jCabfZ116ktwurSXGGu03Msew0_Km_a7IeRnKtFcbkOYwCma4u1wBNdgfBQth5juZQ8gX0nBN5h6O8Y32EF8reiSyNJr4R19rswQmORlL08Zz5IgziCI20pwhirbj_LYZ8fdlO3BzbA4XuEatfx67G7sIiRTfq2Utl-cz0JRysSlKOHA57fJYSaKGgXf4ejTeP65_9Uk0jyUlI-P1-VX-iDAm_f6uf6yp5PMHpB70ZigowCCh-QWtI_I_a5RB41y-zEZe0zQARN0wARFltMBE9RjgjpM0IAJf4LDxBPy5f149u4ji_0zmEa1estKWxkLskxtorPaGmc8qrJGia0LWWS1ACV1ba1JIZGJsqguqdJlqYPKrDY5f0oO2mULzwhFPVXpTOFFcpUXruqjAlBWcvdfWCZwRHhHlUbH4vKux8m3posiXDSBlo2jZRNoeURYP2sViqv85nzREbyJCmJQ_BrEyC9nPv_nmS_I3QHiL8nBdr2DV-SO_r4936xfRzBdAcKbj0s |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Human+trajectory+prediction+and+generation+using+LSTM+models+and+GANs&rft.jtitle=Pattern+recognition&rft.au=Rossi%2C+Luca&rft.au=Paolanti%2C+Marina&rft.au=Pierdicca%2C+Roberto&rft.au=Frontoni%2C+Emanuele&rft.date=2021-12-01&rft.pub=Elsevier+Ltd&rft.issn=0031-3203&rft.eissn=1873-5142&rft.volume=120&rft_id=info:doi/10.1016%2Fj.patcog.2021.108136&rft.externalDocID=S003132032100323X |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0031-3203&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0031-3203&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0031-3203&client=summon |