Skip to main content

Advertisement

SpringerLink
Log in

Adaptive Pose Estimation for Gait Event Detection Using Context-Aware Model and Hierarchical Optimization

  • Original Article
  • Published:
Journal of Electrical Engineering & Technology Aims and scope Submit manuscript

Abstract

To understand daily events accurately, adaptive pose estimation (APE) systems require a robust context-aware model and optimal feature selection methods. In this paper, we propose a novel gait event detection (GED) system that consists of saliency silhouette detection, a robust body parts model and a 2D stick-model followed by a hierarchical optimization algorithm. Furthermore, the most prominent context-aware features such as energy, 0–180° intensity and distinct moveable features are proposed by focusing on invariant and localized characteristics of human postures in different event classes. Finally, we apply Grey Wolf optimization and a genetic algorithm to discriminate complex postures and to provide appropriate labels to each event. In order to evaluate the performance of proposed GED, two public benchmark datasets, UCF101 and YouTube, are examined via the n-fold cross validation method. For the two benchmark datasets, our proposed method detects the human body key points with 82.4% and 83.2% accuracy respectively. Also, it extracts the context-aware features and finally recognizes the gait events with 82.6% and 85.0% accuracy, respectively. Compared with other well-known statistical and state-of-the-art methods, our proposed method outperforms other similarly tasked methods in terms of posture detection and recognition accuracy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Tzelepis C, Ma Z, Mezaris V, Ionescu B, Kompatsiaris I, Boato G, Yan S (2016) Event-based media processing and analysis: a survey of the literature. Image Vis Comput 53:3–19

    Article  Google Scholar 

  2. Susan S, Agrawal P, Mittal M, Bansal S (2019) New shape descriptor in the context of edge continuity. CAAI Trans Intell Technol 4(2):101–109

    Article  Google Scholar 

  3. Tingting Y, Junqian W, Lintai W, Yong X (2019) Three-stage network for age estimation. CAAI Trans Intell Technol 4(2):122–126

    Article  Google Scholar 

  4. Zhu C, Miao D (2019) Influence of kernel clustering on an RBFN. CAAI Trans Intell Technol 4(4):255–260

    Article  Google Scholar 

  5. Wiens T (2019) Engine speed reduction for hydraulic machinery using predictive algorithms. Int J Hydromech 2(1):16–31

    Article  Google Scholar 

  6. Kruk E, Reijne MM (2018) Accuracy of human motion capture systems for sport applications; state-of-the-art review. Eur J Sport Sci 18(6):806–819

    Article  Google Scholar 

  7. Wang Y, Mori G (2008) Multiple tree models for occlusion and spatial constraints in human pose estimation. In: Proceedings on European conference on computer vision Springer, Berlin, Heidelberg, pp 710–724

  8. Amft O, Troster G (2008) Recognition of dietary activity events using on-body sensors. Artif Intell Med 42:121–136

    Article  Google Scholar 

  9. Wang Y, Du B, Shen Y, Wu K (2019) EV-gait: event-based robust gait recognition using dynamic vision sensors. In: Proceedings on CVPR, pp 6358–6367

  10. Jiang Y, Dai G, Mei Q, Rui TY, Chang SF (2015) Super fast event recognition in internet videos. IEEE Trans Multimed 17(8):1174–1186

    Article  Google Scholar 

  11. Liu A-A, Su Y-T, Nie W-Z, Kankanhalli M (2017) Hierarchical clustering multi-task learning for joint human action grouping and recognition. IEEE Trans Pattern Anal Mach Intell 39(1):102–114

    Article  Google Scholar 

  12. Abbasnejad I, Sridharan S, Denman S, Fookes C, Lucey S (2016) Complex event detection using joint max margin and semantic features. In: Proceedings on international conference on digital image computing: techniques and applications (DICTA), gold coast, QLD, pp 1–8

  13. Seemanthini K, Manjunath SS, Srinivasa G, Kiran B, Sowmyasree P (2020) A cognitive semantic-based approach for human event detection in videos. Smart trends in computing and communications, vol 165. Springer, pp 243–253

    Chapter  Google Scholar 

  14. Meng Q, Zhu H, Zhang W, Piao X, Zhang A (2020) Action recognition using form and motion modalities. ACM Trans Multimed Comput Commun Appl 16(1):1–22

    Article  Google Scholar 

  15. Mahmood M, Jalal A, Kim K (2019) WHITE STAG model: wise human interaction tracking and estimation (WHITE) using spatio-temporal and angular-geometric (STAG) descriptors. Multimed Tools Appl 79:1–32

    Google Scholar 

  16. Ahmed A, Jalal A, Kim K (2020) A novel statistical method for scene classification based on multi-object categorization and logistic regression. Sensors 20(14):3871

    Article  Google Scholar 

  17. Osterland S, Weber J (2019) Analytical analysis of single-stage pressure relief valves. Int J Hydromech 2(1):32–53

    Article  Google Scholar 

  18. Li H, Lu H, Lin Z, Shen X, Price B (2015) Inner and inter label propagation: salient object detection in the wild. IEEE Trans Image Process 24(10):3176–3186

    Article  MathSciNet  Google Scholar 

  19. Moschini D, Fusiello A (2009) Tracking human motion with multiple cameras using an articulated model. In: Proceedings on computer vision/computer graphics collaboration techniques and applications, pp 1–12

  20. Shokri M, Tavakoli K (2019) A review on the artificial neural network approach to analysis and prediction of seismic damage in infrastructure. Int J Hydromech 2(4):178–196

    Article  Google Scholar 

  21. Quaid MAK, Jalal A (2009) Wearable sensors based human behavioral pattern recognition using statistical features and reweighted genetic algorithm. Multimed Tools Appl, pp 1–23

  22. Donahue J, Anne Hendricks L, Guadarrama S, Rohrbach M, Venugopalan S, Saenko K, Darrell T (2015) Long-term recurrent convolutional networks for visual recognition and description. In: Proceedings of computer vision and pattern recognition, pp 2625–2634

  23. Srivastava N, Mansimov E, Salakhudinov R (2015) Unsupervised learning of video representations using lstms. In: Proceedings on international conference on machine learning, pp 843–852

  24. Liu J, Luo J, Shah M (2009) Recognizing realistic actions from videos in the wild. In: Proceedings on IEEE conference on computer vision and pattern recognition, pp 1996–2003

  25. Ikizler-Cinbis N, Sclaroff S (2010) Object, scene and actions: combining multiple features for human action recognition. In: Proceedings on European conference on computer vision, pp 494–507

  26. Shreyas D, Raksha S, Prasad B (2020) Implementation of an anomalous human activity recognition system. SN Comput Sci 1(168):158–168

  27. Zheng Z, An G, Ruan Q (2017) Multi-level recurrent residual networks for action recognition. ArXiv preprint arXiv:1711.08238

  28. Wang H, Kläser A, Schmid C, Cheng-Lin L (2011) Action recognition by dense trajectories. In: Proceedings on CVPR, pp 3169–3176

Download references

Acknowledgements

This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (No. 2018R1D1A1A02085645). Also, this work was supported by the Korea Medical Device Development Fund grant funded by the Korea government (the Ministry of Science and ICT, the Ministry of Trade, Industry and Energy, the Ministry of Health & Welfare, the Ministry of Food and Drug Safety) (Project Number: 202012D05-02).

Author information

Authors and Affiliations

  1. Department of Computer Science, Air University, Islamabad, Pakistan

    Israr Akhter & Ahmad Jalal

  2. Department of Human-Computer Interaction, Hanyang University, Seoul, South Korea

    Kibum Kim

Authors
  1. Israr Akhter
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ahmad Jalal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kibum Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kibum Kim.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Akhter, I., Jalal, A. & Kim, K. Adaptive Pose Estimation for Gait Event Detection Using Context-Aware Model and Hierarchical Optimization. J. Electr. Eng. Technol. 16, 2721–2729 (2021). https://doi.org/10.1007/s42835-021-00756-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42835-021-00756-y

Keywords

Search

Navigation