Abstract
Human Activity Recognition is the process of identifying the activity of a person by analyzing continuous frames of a video. In many application areas, human activity identification is either a direct goal or it is a key segment of a bigger objective. Some of the examples are surveillance system, elder healthcare monitoring system, abnormal activity detection systems such as fight detection, theft detection etc. Robust and accurate activity recognition is a challenging task due to diverse reasons, such as changing ambient illumination, noise, background turbulence, camera placements etc. Existing literatures discuss some techniques for identifying human activity but these approaches are restricted to the case of videos recorded from static camera. The aim of the proposed approach is to fill this gap. In this proposed method, a new skeleton based feature for human activity recognition- “Orientation Invariant Skeleton Feature (OISF)”- is introduced and used to train Random Forest (RF) classifier for Human Activity Recognition. Efficiency of newly introduced feature OISF is analyzed for the videos recorded with multiple cameras positioned at two different slant angles. Experimental results reveal that the newly introduced feature OISF has minimal dependency on variations of camera orientation. Accuracy achieved is ≈ 99.30% with ViHASi dataset, ≈ 96.85% with KTH dataset and ≈ 98.34% with in-house dataset which is higher than those achieved by other researches with existing features. The improved result of human activity recognition in terms of accuracy proves the appropriateness of the proposed research in being used commercially.
Similar content being viewed by others
References
Agarwal JK, Ryoo MS (2011) Human activity analysis: a review. ACM Comput Surv (CSUR) 43(3): 1–43
Anjum ML, Rosa S, Bona B (2017) Tracking a subset of skeleton joints: an effective approach towards complex human activity recognition. Journal of Robotics
Bächlin M, Forster K, Troster G (2009) SwimMaster: a wearable assistant for swimmer. In: Proceedings of the 11th international conference on ubiquitous computing, pp 215–224
Breiman L (2001) Random forests. Mach Learn 45(1):5–32
Breiman L (1996) Bagging predictors. Mach Learn 24(2):123–140
Chen MY, Hauptmann A (2009) Mosift: recognizing human actions in surveillance videos. Citeseer
Singh DK, Kushwaha DS (2016) Tracking movements of humans in a real-time surveillance scene. In: Proceedings of fifth international conference on soft computing for problem solving, pp 491–500
Dawn DD, Shaikh SH (2016) A comprehensive survey of human action recognition with spatio-temporal interest point (STIP) detector. Vis Comput Springer 32(3):289–306
Du Y, Wang W, Wang L (2015) Hierarchical recurrent neural network for skeleton based action recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1110–1118
Gilbert A, Illingworth J, Bowden R (2009) Fast realistic multi-action recognition using mined dense spatio-temporal features. In: 2009 IEEE 12th international conference on computer vision, pp 925–931
Hbali Y, Hbali S, Ballihi L, Sadgal M (2017) Skeleton-based human activity recognition for elderly monitoring systems. IET Comput Vis 12(1):16–26
Ho TK (1995) Random decision forests. In: Proceedings of 3rd international conference on document analysis and recognition, vol 1. IEEE, pp 278–282
Jalal A, Uddin MZ, Kim JT, Kim TS (2012) Recognition of human home activities via depth silhouettes and R transformation for smart homes. Indoor Built Environ 21(1):184–190
Jalal A, Kamal S, Kim D (2017) A depth video-based human detection and activity recognition using multi-features and embedded hidden Markov models for health care monitoring systems. Int J Interact Multimed Artif Intell 4:4
Jalaland A, Kamal S (2014) Real-time life logging via a depth silhouette-based human activity recognition system for smart home services. In: 2014 11th IEEE International conference on advanced video and signal based surveillance (AVSS), pp 74–80
Kovashka A, Grauman K (2010) Learning a hierarchy of discriminative space-time neighborhood features for human action recognition. In: 2010 IEEE computer society conference on computer vision and pattern recognition, pp 2046–2053
Kumar S, Kumar S, Raman B, Sukavanam N (2011) Human action recognition in a wide and complex environment. Real-Time Image Video Process 7871:78710I
Lassoued I, Zagrouba E (2018) Human actions recognition: an approach based on stable motion boundary fields. Multimed Tools Appl 77(16):20715–20729
Li M, Leung H (2016) Multiview skeletal interaction recognition using active joint interaction graph. IEEE Trans Multimed 18(11):2293–2302
Lu M, Zhang L (2014) Action recognition by fusing spatial-temporal appearance and the local distribution of interest points. In: International conference on future computer and communication engineering (ICFCCE 2014)
Manresa C, Varona J, Mas R, Perales FJ (2005) Hand tracking and gesture recognition for human-computer interaction. ELCVIA Electron Lett Comput Vis Image Anal 5(3):96–104
Manzi A, Fiorini L, Limosani R, Dario P, Cavallo F (2017) Two-person activity recognition using skeleton data. IET Comput Vis 12(1):27–35
Min W, Cui H, Rao H, Li ZZ, Yao L (2018) Detection of human falls on furniture using scene analysis based on deep learning and activity characteristics. IEEE Access 6:9324–9335
Naveed H, Khan G, Khan AU, Siddiqi A, Khan MUG (2019) Human activity recognition using mixture of heterogeneous features and sequential minimal optimization. Int J Mach Learn Cybern 10(9):2329–2340
Ofli F, Chaudhry R, Kurillo G, Vidal R, Bajcsy R (2014) Sequence of the most informative joints (SMIJ): a new representation for human skeletal action recognition. J Vis Commun Image Represent 25(1):24–38
Quaid MAK, Jalal A (2019) Wearable sensors based human behavioral pattern recognition using statistical features and reweighted genetic algorithm. Multimed Tools Appl, 1–23
Ragheb H, Velastin S, Remagnino P, Ellis T (2008) ViHASi: virtual human action silhouette data for the performance evaluation of silhouette-based action recognition methods. In: Second ACM/IEEE international conference on distributed smart cameras. IEEE, pp 1–10
Raptis M, Sigal L (2013) Poselet key-framing: a model for human activity recognition. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 2650–2657
Sadek S, Al-Hamadi A, Gerald K, Michaelis B (2013) Affine-invariant feature extraction for activity recognition. ISRN Mach Vis, 2013
Schapire RE, Freund Y, Bartlett P, Lee WS (1998) Boosting the margin: a new explanation for the effectiveness of voting methods. Annals Stat 26(5):1651–1686
Schuldt C, Laptev I, Caputo B (2004) Recognizing human actions: a local SVM approach. In: Proceedings of the 17th international conference on pattern recognition (ICPR). IEEE, pp 32–36
Shah H, Chokalingam P, Paluri B, Pradeep N, Raman B (2007) Automated stroke classification in tennis. In: International conference image analysis and recognition, pp 1128–1137
Uddin MZ, Lee JJ, Kim TS (2010) Independent shape component-based human activity recognition via hidden Markov model. Appl Intell 33(2):193–206
Vats E, Chan CS (2016) Early detection of human actions—a hybrid approach. Appl Soft Comput 46:953–966
Wang H, Schmid C (2013) Action recognition with improved trajectories. In: Proceedings of the IEEE international conference on computer vision, pp 3551–3558
Wang H, Kläser A, Schmid C, Lin-Cheng L (2011) Action recognition by dense trajectories. In: CVPR 2011-IEEE conference on computer vision & pattern recognition, pp 3169–3176
Weng Z, Guan Y (2018) Action recognition using length-variable edge trajectory and spatio-temporal motion skeleton descriptor. EURASIP J Image Video Process 2018 (1):8
Xu K, Jiang X, Sun T (2015) Human activity recognition based on pose points selection. In: 2015 IEEE International conference on image processing (ICIP), pp 2930–2834
Zhu C, Sheng W (2011) Wearable sensor-based hand gesture and daily activity recognition for robot-assisted living. IEEE Trans Syst Man Cybern-Part A: Syst Humans 41(3):569–573
Zhu W, Lan C, Xing J, Zeng W, Li Y, Shen L, Xie X (2016) Co-occurrence feature learning for skeleton based action recognition using regularized deep LSTM networks. In: AAAI Conference on artificial intelligence, p 8
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendix: Details of Algorithms
Appendix: Details of Algorithms
Rights and permissions
About this article
Cite this article
Dwivedi, N., Singh, D.K. & Kushwaha, D.S. Orientation Invariant Skeleton Feature (OISF): a new feature for Human Activity Recognition. Multimed Tools Appl 79, 21037–21072 (2020). https://doi.org/10.1007/s11042-020-08902-w
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-020-08902-w