Impact Factor:6.549
 Scopus Suggested Journal: UNDER REVIEW for TITLE INCLUSSION

International Journal
of Computer Engineering in Research Trends (IJCERT)

Scholarly, Peer-Reviewed, Open Access and Multidisciplinary


Welcome to IJCERT

International Journal of Computer Engineering in Research Trends. Scholarly, Peer-Reviewed,Open Access and Multidisciplinary

ISSN(Online):2349-7084                 Submit Paper    Check Paper Status    Conference Proposal

Back to Current Issues

Review: Pedestrian Behavior Analysis and Trajectory Prediction with Deep Learning

Mr M.Bhavsingh, Mr. B .Pannalal, Mrs. K Samunnisa, ,
Affiliations
Assistant Professor, Ashoka Women's Engineering College, Kurnool
:10.22362/ijcert/2022/v9/i12/v9i1205


Abstract
It is becoming increasingly necessary for artificially intelligent systems to be able to monitor, evaluate, and anticipate the actions of humans as more of these systems are deployed in human-populated places. For an autonomous vehicle to make intelligent navigation decisions, a comprehensive analysis of the movement patterns of surrounding traffic agents and precise projections of their future trajectories are required. In this paper, we investigate how to assess the behavior of pedestrians and predict their trajectories using a unified deep learning model. Specifically, we look at how to do both of these things. Investigate the methods that were utilized to collect data and evaluate performance, as well as any surprises, challenges, insights, and recommendations that occurred as a result of the investigation's findings


Citation
Mr M.Bhavsingh,Mr. B .Pannalal,Mrs. K Samunnisa."Review: Pedestrian Behavior Analysis and Trajectory Prediction with Deep Learning". International Journal of Computer Engineering In Research Trends (IJCERT) ,ISSN:2349-7084 ,Vol.9, Issue 12,pp.263-268, December - 2023. https://ijcert.org/ems/ijcert_papers/V9I1204.pdf


Keywords : Trajectory Prediction, Pedestrian Behavior Analysis, deep learning

References
[1] Korbmacher, R., & Tordeux, A. : Review of Pedestrian Trajectory Prediction Methods: Comparing Deep Learning and Knowledge-based Approaches. ArXiv, abs /(2022). 2111.06740.
[2]  Yi, S., Li, H., Wang, X.: Understanding pedestrian behaviors from stationary crowd groups. In: Proceedings of CVPR (2019)
[3] Yi, S., Li, H., Wang, X.: Pedestrian behavior modeling from stationary crowds with applications to intelligent surveillance. TIP 25(9), 4354–4368 (2016).
[4]  Tang, S., Andriluka, M., Milan, A., Schindler, K., Roth, S., Schiele, B.: Learning people detectors for tracking in crowded scenes. In: Proceedings of ICCV (2016)
[5] Leal-Taix´e, L., Fenzi, M., Kuznetsova, A., Rosenhahn, B., Savarese, S.: Learning an image-based motion context for multiple people tracking. In: Proceedings of 	CVPR (2017)
[6]Zhou, B., Wang, X., Tang, X.: Understanding collective crowd behaviors: learning a mixture model of dynamic pedestrian-agents. In: Proceedings of CVPR (2019)
[7] Chang, M.C., Krahnstoever, N., Ge, W.: Probabilistic group-level motion analysis and scenario recognition. In: Proceedings of ICCV (2018).
[8] Mehran, R., Oyama, A., Shah, M.: Abnormal crowd behavior detection using social force model. In: Proceedings of CVPR (2017)
[9] Mahadevan, V., Li, W., Bhalodia, V., Vasconcelos, N.: Anomaly detection in crowded scenes. In: Proceedings of CVPR (2010)
[10] Yi, S., Wang, X., Lu, C., Jia, J., Li, H.: L0 regularized stationary-time estimation for crowd analysis. TPAMI PP(99), 1 (2016).
[11] Pellegrini, S., Ess, A., Schindler, K., Van Gool, L.: You’ll never walk alone: modeling social behavior for multi-target tracking. In: Proceedings of ICCV (2019)
[12] Xiaoge Zhang, Sankaran Mahadevan, Bayesian neural networks for flight trajectory prediction and safety assessment, Decision Support Systems, Volume 131,2020,113246, ISSN 0167-9236.
[13] S. Lefèvre, C. Laugier and J. Ibañez-Guzmán, "Exploiting map information for driver intention estimation at road intersections," 2011 IEEE Intelligent Vehicles Symposium (IV), Baden-Baden, 2011, pp. 583-588, doi: 10.1109/IVS.2011.5940452.
[14] S. Danielsson, L. Petersson and A. Eidehall, "Monte Carlo based Threat Assessment: Analysis and Improvements," 2007 IEEE Intelligent Vehicles Symposium, Istanbul, 2007, pp. 233-238, doi: 10.1109/IVS.2007.4290120.
[15] Q. Tran and J. Firl, "A probabilistic discriminative approach for situation recognition in traffic scenarios," 2012 IEEE Intelligent Vehicles Symposium, Alcala de Henares, 2012, pp. 147-152, doi: 10.1109/IVS.2012.6232279.
[16] C. E. Rasmussen & C. K. I. Williams, Gaussian Processes for Machine Learning, the MIT Press, 2006, ISBN 026218253X.
[17] Wenjie Luo, Bin Yang, and Raquel Urtasun. Fast and furious: Real time end-to-end 3d detection, tracking and motion forecasting with a single convolutional net. In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pages 3569–3577, 2018. 
[18] Amir Sadeghian, Vineet Kosaraju, Ali Sadeghian, Noriaki Hirose, Hamid Rezatofighi, and Silvio Savarese. Sophie: An attentive gan for predicting paths compliant to social and physical constraints. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 1349– 1358, 2019.
[19] Nachiket Deo and Mohan M Trivedi. Convolutional social pooling for vehicle trajectory prediction. arXiv preprint arXiv:1805.06771, 2018.
[20] Rohan Chandra, Uttaran Bhattacharya, Aniket Bera, and Dinesh Manocha. Traphic: Trajectory prediction in dense and heterogeneous traffic using weighted interactions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 8483–8492, 2019
[21] Rohan Chandra, Uttaran Bhattacharya, Christian Roncal, Aniket Bera, and Dinesh Manocha. Robusttp: End-toend trajectory prediction for heterogeneous road-agents in dense traffic with noisy sensor inputs. arXiv preprint arXiv:1907.08752, 2019.
[22] Yuexin Ma, Xinge Zhu, Sibo Zhang, Ruigang Yang, Wenping Wang, and Dinesh Manocha. Trafficpredict: Trajectory prediction for heterogeneous traffic-agents. arXiv preprint arXiv:1811.02146, 2018.
[23] N. Djuric, V. Radosavljevic, H. Cui, T. Nguyen, F.-C. Chou, T.-H. Lin, and J. Schneider. Short-term Motion Prediction of Traffic Actors for Autonomous Driving using Deep Convolutional Networks. ArXiv e-prints, Aug. 2018.
[24] Shengnan Guo, Youfang Lin, Ning Feng, Chao Song, and Huaiyu Wan. Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 33, pages 922–929, 2019.
[25] Zhiyong Cui, Kristian Henrickson, Ruimin Ke, and Yinhai Wang. Traffic graph convolutional recurrent neural network: A deep learning framework for network-scale traffic learning and forecasting. arXiv preprint arXiv:1802.07007, 2018.
[26] Kooij, Julian & Schneider, Nicolas & Flohr, Fabian & Gavrila, Dariu. (2014). Context-Based Pedestrian Path Prediction. 10.1007/978-3-319-10599-4_40.
[27] S. K. Jayaraman, C. Creech, L. P. Robert Jr., D. M. Tilbury, X. J. Yang, A. K. Pradhan, and K. M. Tsui, “Trust in AV: An Uncertainty Reduction Model of AV-Pedestrian Interactions,” in Companion 2018 ACM/IEEE Int. Conf. Human-Robot Interaction, 2018, pp. 133–134. 
[28] L. Robert, “The future of pedestrian–automated vehicle interactions,” XRDS: Crossroads, ACM, vol. 25, no. 3, 2019.
[29] K. Saleh, M. Hossny, and S. Nahavandi, “Towards trusted autonomous vehicles from vulnerable road users perspective,” in 2017 11th Annu. IEEE Int. Syst. Conf., SysCon, 2017, pp. 1–7.
 [30] A. Gorrini, G. Vizzari, and S. Bandini, “Towards Modelling Pedestrian-Vehicle Interactions: Empirical Study on Urban Unsignalized Intersection,” arXiv preprint arXiv:1610.07892, 2016. 
[31] A. Alahi, K. Goel, V. Ramanathan, A. Robicquet, L. Fei-Fei, and S. Savarese, “Social LSTM: Human Trajectory Prediction in Crowded Spaces,” in Proc. IEEE Conf. Comput. Vision Pattern Recognition, 2016, pp. 961–971.
[32] Yi, S., Li, H., & Wang, X. (2016). Pedestrian Behavior Understanding and Prediction with Deep Neural Networks. Lecture Notes in Computer Science, 263–279.
[33] Basharat, A., Gritai, A., Shah, M.: Learning object motion patterns for anomaly detection and improved object detection. In: Proceedings of CVPR (2008)
[34] Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of CVPR (2015) 
[35] Simonyan, K., Zisserman, A.: Two-stream convolutional networks for action recognition in videos. In: Proceedings of NIPS (2014) 
[36] Shao, J., Kang, K., Loy, C.C., Wang, X.: Deeply learned attributes for crowded scene understanding. In: Proceedings of CVPR (2015)
[37]. Ji, S., Xu, W., Yang, M., Yu, K.: 3D convolutional neural networks for human action recognition. TPAMI 35(1), 221–231 (2013)


DOI Link : https://doi.org/10.22362/ijcert/2022/v9/i12/v9i1205

Download :
  V9I1205.pdf


Refbacks : Currently there are no Refbacks

Announcements


Authors are not required to pay any article-processing charges (APC) for their article to be published open access in Journal IJCERT. No charge is involved in any stage of the publication process, from administrating peer review to copy editing and hosting the final article on dedicated servers. This is free for all authors. 

News & Events


Latest issue :Volume 10 Issue 1 Articles In press

A plagiarism check will be implemented for all the articles using world-renowned software. Turnitin.


Digital Object Identifier will be assigned for all the articles being published in the Journal from September 2016 issue, i.e. Volume 3, Issue 9, 2016.


IJCERT is a member of the prestigious.Each of the IJCERT articles has its unique DOI reference.
DOI Prefix : 10.22362/ijcert


IJCERT is member of The Publishers International Linking Association, Inc. (“PILA”)


Emerging Sources Citation Index (in process)


IJCERT title is under evaluation by Scopus.


Key Dates


☞   INVITING SUBMISSIONS FOR THE NEXT ISSUE :
☞   LAST DATE OF SUBMISSION : 31st March 2023
☞  SUBMISSION TO FIRST DECISION :
In 7 Days
☞  FINAL DECISION :
IN 3 WEEKS FROM THE DAY OF SUBMISSION

Important Announcements


All the authors, conference coordinators, conveners, and guest editors kindly check their articles' originality before submitting them to IJCERT. If any material is found to be duplicate submission or sent to other journals when the content is in the process with IJCERT, fabricated data, cut and paste (plagiarized), at any stage of processing of material, IJCERT is bound to take the following actions.
1. Rejection of the article.
2. The author will be blocked for future communication with IJCERT if duplicate articles are submitted.
3. A letter regarding this will be posted to the Principal/Director of the Institution where the study was conducted.
4. A List of blacklisted authors will be shared among the Chief Editors of other prestigious Journals
We have been screening articles for plagiarism with a world-renowned tool: Turnitin However, it is only rejected if found plagiarized. This more stern action is being taken because of the illegal behavior of a handful of authors who have been involved in ethical misconduct. The Screening and making a decision on such articles costs colossal time and resources for the journal. It directly delays the process of genuine materials.

Citation Index


Citations Indices All
Citations 1026
h-index 14
i10-index 20
Source: Google Scholar

Acceptance Rate (By Year)


Acceptance Rate (By Year)
Year Rate
2021 10.8%
2020 13.6%
2019 15.9%
2018 14.5%
2017 16.6%
2016 15.8%
2015 18.2%
2014 20.6%

Important Links



Conference Proposal




DOI:10.22362/ijcert