Abstract
AI and computer vision are making strides everyday with newer solutions to real-world problems and emerging challenges. An extremely important and useful application of computer vision is surveillance systems for security in open or closed spaces. With this research work, we investigate state-of-the-art works for forecasting pedestrian trajectory using videos. We present a comprehensive review of literature and compare the various techniques along with their pros and cons that have been used for this application.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Alahi A et al (2016) Social LSTM: human trajectory prediction in crowded spaces. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 961–971
Amirian J, Hayet J-B, Pettré J (2019) Social ways: learning multi-modal distributions of pedestrian trajectories with GANs. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops
Ballan L et al (2016) Knowledge transfer for scene-specific motion prediction. In: European conference on computer vision. Springer, pp 697–713
Bartoli F et al (2016) Context-aware trajectory prediction. In: 2018 24th international conference on pattern recognition (ICPR). IEEE, pp 1941–1946
Bisagno N, Zhang B, Conci N (2018) Group LSTM: group trajectory prediction in crowded scenarios. In: Proceedings of the European conference on computer vision (ECCV) workshops
Butenuth M et al (2011) Integrating pedestrian simulation, tracking and event detection for crowd analysis. In: 2011 IEEE international conference on computer vision workshops (ICCV workshops). IEEE, pp 150–157
Chandra R et al (2019) TraPHic: trajectory prediction in dense and heterogeneous traffic using weighted interactions, pp 8475– 8484. https://doi.org/10.1109/CVPR.2019.00868
Chen Y et al (2020) CoMoGCN: coherent motion aware trajectory prediction with graph representation. ar**v preprint ar**v:2005.00754
Eiffert S et al (2020) Probabilistic crowd GAN: multimodal pedestrian trajectory prediction using a graph vehicle-pedestrian attention network. IEEE Robot Autom Lett 5(4):5026–5033. https://doi.org/10.1109/LRA.2020.3004324
Fernando T et al (2018) GD-GAN: generative adversarial networks for trajectory prediction and group detection in crowds. In: Asian conference on computer vision. Springer, pp 314–330
Gupta A et al (2018) Social GAN: socially acceptable trajectories with generative adversarial networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2255–2264
Haddad S, Lam S-K (2020) Self-growing spatial graph networks for pedestrian trajectory prediction. In: Proceedings of the IEEE/CVF Winter conference on applications of computer vision, pp 1151–1159
Huang Y et al (2019) STGAT: modeling spatial-temporal interactions for human trajectory prediction. In: Proceedings of the IEEE/CVF international conference on computer vision, pp 6272–6281
Kosaraju V et al (2019) Social-BIGAT: multimodal trajectory forecasting using bicycle-GAN and graph attention networks. Adv Neur Inf Process Syst 32
Kratzwald B et al (2017) Improving video generation for multi-functional applications. ar**v preprint ar**v:1711.11453
Li X et al (2020) A recurrent attention and interaction model for pedestrian trajectory prediction. IEEE/CAA J Autom Sinica 7(5):1361–1370
Liang J et al (2019) Peeking into the future: predicting future person activities and locations in videos. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 5725–5734
Sadeghian A et al (2019) Sophie: an attentive GAN for predicting paths compliant to social and physical constraints. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 1349– 1358
Shafiee N, Padir T, Elhamifar E (2021) Introvert: human trajectory prediction via conditional 3D attention. In: 2021 IEEE/CVF conference on computer vision and pattern recognition (CVPR), pp 16810–16820. https://doi.org/10.1109/CVPR46437.2021.01654
Sharma R, Guha T (2016) A trajectory clustering approach to crowd flow segmentation in videos. In: 2016 IEEE international conference on image processing (ICIP). IEEE, pp 1200–1204
Shi Y, Liu W, **ng W (2020) Crowd trajectory prediction based on surveillance data. In: Proceedings of the 2020 international conference on computing, networks and internet of things, pp 217–221
Song X et al (2020) Pedestrian trajectory prediction based on deep convolutional LSTM network. IEEE Trans Intell Transp Syst 22(6):3285–3302
Xu Y, Piao Z, Gao S (2018) Encoding crowd interaction with deep neural network for pedestrian trajectory prediction. In: Proceedings of the IEEE conference CVPR, pp 5275–5284
Xue H, Huynh DQ, Reynolds M (2018) SS-LSTM: a hierarchical LSTM model for pedestrian trajectory prediction. In: 2018 IEEE WACV. IEEE, pp 1186–1194
Yi S, Li H, Wang X (2016) Pedestrian behavior modeling from stationary crowds with applications to intelligent surveillance. IEEE Trans Image Process 25(9):4354–4368
Zhang P et al (2019) SR-LSTM: state refinement for LSTM towards pedestrian trajectory prediction. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 12085–12094
Zhu Y et al (2019) Starnet: pedestrian trajectory prediction using deep neural network in star topology. In: 2019 IEEE/RSJ international conference on intelligent robots and systems (IROS). IEEE, pp 8075–8080
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Bansal, A., Agarwal, A., Lalit, M., Seeja, K.R. (2023). Survey of Pedestrian Trajectory Prediction Techniques Using Surveillance Videos. In: Chakraborty, B., Biswas, A., Chakrabarti, A. (eds) Advances in Data Science and Computing Technologies. ADSC 2022. Lecture Notes in Electrical Engineering, vol 1056. Springer, Singapore. https://doi.org/10.1007/978-981-99-3656-4_64
Download citation
DOI: https://doi.org/10.1007/978-981-99-3656-4_64
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-3655-7
Online ISBN: 978-981-99-3656-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)