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Investigating image stitching for action recognition

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Abstract

Action recognition is usually a central problem for many practical applications, such as video annotations, video surveillance and human computer interaction. Most action recognition approaches are based on localized spatio-temporal features that can vary significantly when the viewpoint changes. However, their performance rapidly drops when the viewpoints of the training and testing data are different. In this paper, we propose a transfer learning framework for view-invariant action recognition by the way of sharing image stitching feature among different views. Experimental results on multi-view action recognition IXMAS dataset demonstrate that our method produces remarkably good results and outperforms baseline methods.

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  1. http://www.csie.ntu.edu.tw/cjlin/libsvm/

References

  1. Chang X, Ma Z, Lin M, Yang Y, Hauptmann A (2017) Feature interaction augmented sparse learning for fast kinect motion detection. In: IEEE transactions on image processing

    Google Scholar 

  2. Chang X, Ma Z, Yang Y, Zeng Z (2017) Hauptmann AG Bi-level semantic representation analysis for multimedia event detection. In: IEEE transactions on cybernetics

    Google Scholar 

  3. Chang X, Nie F, Wang S, Yang Y, Zhou X, Zhang C (2017) Compound rank-k projections for bilinear analysis. In: IEEE transactions on neural networks and learning systems

    Google Scholar 

  4. Farhadi A, Tabrizi MK (2008) Learning to recognize activities from the wrong view point. In: ECCV

    Google Scholar 

  5. Huang CH, Yeh YR, Wang YCF (2012) Recognizing actions across cameras by exploring the correlated subspace. In: ECCV

    Google Scholar 

  6. Li B, Camps OI, Sznaier M (2012) Cross-view activity recognition using hankelets. In: CVPR

    Google Scholar 

  7. Li R, Zickler T (2012) Discriminative virtual views for cross-view action recognition. In: CVPR

    Google Scholar 

  8. Liu J, Shah M, Kuipers B, Savarese S (2011) Cross-view action recognition via view knowledge transfer. In: CVPR

    Google Scholar 

  9. Reddy K, Liu J, Shah M (2009) Incremental action recognition using feature tree. In: ICCV

    Google Scholar 

  10. Sugiyama M, Nakajima S, Kawanabe M (2008) Direct importance estimation with model selection and its application to covariate shift adaptation. In: NIPS

    Google Scholar 

  11. Tuzal O, Porikli F, Meer P (2008) Pedestrian detection via classification on riemannian manifolds. IEEE Trans Pattern Anal Mach Intell 30(10):1713–1727

    Article  Google Scholar 

  12. Viola P, Jones M (2001) Rapid object detection using a boosted cascade of simple features. In: CVPR

    Google Scholar 

  13. Weinland D, Boyer E, Ronfard R (2007) Action recognition from arbitrary views using 3d exemplars. In: ICCV

    Google Scholar 

  14. Weinland D, Ronfard R, Boyer E (2011) A survey of vision-based methods for action representation, segmentation and recognition. Comput Vis Image Underst 115:224–241

    Article  Google Scholar 

  15. Wu X, Jia Y (2012) View-invariant action recognition using latent kernelized structural SVM. In: ECCV

    Google Scholar 

  16. Yan Y, Ricci E, Subramanian R, Lanz O, Sebe N (2013) No matter where you are: flexible graph-guided multi-task learning for multi-view head pose classification under target motion. In: IEEE international conference on computer vision

    Google Scholar 

  17. Yan Y, Ricci E, Subramanian R, Liu G, Sebe N (2014) Multitask linear discriminant analysis for view invariant action recognition. In: IEEE transactions on image processing

    Google Scholar 

  18. Yan Y, Xu C, Cai D, Corso J (2017) Weakly supervised actor-action segmentation via robust multi-task ranking. In: IEEE conference on computer vision and pattern recognition

    Google Scholar 

Download references

Acknowledgements

This research is supported by the Scientific Research Project of Hunan Provincial Education Department, China. The research and application of UAV Aerial system based on image splicing using multiple cameras (Grant No. 16C1139).

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Correspondence to Lixin Tan.

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Li, Y., Li, P., Lei, D. et al. Investigating image stitching for action recognition. Multimed Tools Appl 77, 3279–3286 (2018). https://doi.org/10.1007/s11042-017-5072-4

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  • DOI: https://doi.org/10.1007/s11042-017-5072-4

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