Recognition of vision-based activities of daily living using linear predictive coding of histogram of directional derivative

  • Sidharth B. BhorgeEmail author
  • Ramchandra R. Manthalkar
Original Research


In this paper, we have introduced a novel approach for recognition of activities of daily living (ADL). These activities are the ones that the human beings perform in daily life. At the object level, we used computational color model for efficient object segmentation and tracking to handle dynamic background change in indoor environment. To make it computationally efficient, cosine of the angle between the expected image color vector and current image color vector is used. At feature level, we have presented a linear predictive coding of histogram of directional derivative as a spatio-temporal descriptor. Our proposed descriptor describes the local object shape and appearance within cuboids effectively and distinctively. A multiclass support vector machine has been used to classify the human activities. The proposed framework for recognition of indoor human activity has been extensively validated on the benchmark of ADL datasets, with a focus that this methodology is robust and attains more precise human activity recognition rate as compared to current methodologies available.


Human activity recognition Activities of daily living Histogram of directional derivative 


  1. Ahmad M, Lee SW (2008) Recognizing human actions based on silhouette energy and global motion description. In: 8th IEEE interntional conference on automatic face & gesture recognition.
  2. Amiribesheli M, Benmansour A, Bouchachia A (2015) A review of smart homes in healthcare. J Ambient Intell Hum Comp 6(4):495–517Google Scholar
  3. Banerjee P, Nevatia R (2011) Learning neighborhood co-occurrence statistics of sparse features for human activity recognition. In: 8th IEEE interntional conference advanced video signal based surveill, pp 212–217.
  4. Benesty J, Chen J, Huang Y (2008) Linear prediction. In: Springer handbook of speech processing, pp 121–134.
  5. Bobick A, Davis J (2001) The recognition of human movement using temporal templates. IEEE Trans Pattern Anal Mach Intell 23:257–267CrossRefGoogle Scholar
  6. Chang C-C, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM Trans Intell syst and Tech 2(3):1–27Google Scholar
  7. Cucchiara R, Granan C, Piccardi M, Prati A (2003) Detecting moving objects, ghosts and shadows in video stream. IEEE Trans Pattern Anal Mach Intell 25(10):1337–1342CrossRefGoogle Scholar
  8. Dalal N, Triggs B (2005) Histogram of oriented gradients for human detection. In: IEEE conference on computer vision pattern recognition, pp 886–893Google Scholar
  9. Derpanis K, Sizintsev M, Cannons K, Wildes R (2013) Action spotting and recognition based on a spatiotemporal orientation analysis. IEEE Trans Pattern Anal Mach Intell 35:527–540CrossRefGoogle Scholar
  10. Dollar P, Rabaud V, Cottrel G (2005) Behavior recognition via sparse spatio-temporal features. In: IEEE international workshop on VS-PETSGoogle Scholar
  11. Fleury A, Vacher M, Nouey N (2010) SVM-based multimodal classification of activities of daily living in health smart homes: sensors, algorithms and first experimental results. IEEE Trans Inf Technol Biomed 14(2):274–283CrossRefGoogle Scholar
  12. Ghamadi M, Zhang L, Gotoh Y (2012) Spatio-temporal SIFT and its application to human action classification. In: Euroean conference on computer vision, pp 301–310Google Scholar
  13. Gorelick L, Blank M, Shechtman E, Irani M, Basri R (2007) Actions as space–time shapes. IEEE Trans Pattern Anal Mach Intell 29(12):2247–2253CrossRefGoogle Scholar
  14. Haritaoglu I, Harwood D, Davis L, S (2000) W4: real-time surveillance of people and their activities. IEEE Trans Pattern Anal Mach Intell 22(8):809–830CrossRefGoogle Scholar
  15. Horprasert T, Harwood D, Davis L (1999) A statistical approach for real-time robust background subtraction and shadow detection. In: IEEE ICCV’ 99FRAME_RATE WORKSHOP, Kerkyra GreeceGoogle Scholar
  16. Ikizler N, Duygulu P (2009) histogram of oriented rectangles: a new pose descriptor for human action recognition. Image Vis Comput 27(10):1515:1526CrossRefGoogle Scholar
  17. Javier R, Kim J, Y. (2014) Application of linear predictive coding for human activity classification based on micro-Doppler signatures. IEEE Geosci Remote Sens Lett 11(10):1831–1834Google Scholar
  18. Jhuang H, Serre T, Wolf L, Poggio T (2007) A biologically inspired system for action recognition. Proceedings IEEE international conference on computer vision, pp 1–8Google Scholar
  19. Klaser A, Marszalek M, Schmid C (2008) A spatio-temporal descriptor based on 3D-gradients. In: British machine vision international conferenceGoogle Scholar
  20. Konstantinos A, Briassouli A, Loannis K (2015) Activities of daily living recognition using optimal trajectories from motion boundaries. J Ambient Intell Smart Environ 7(6):817–834CrossRefGoogle Scholar
  21. Laptev I (2005) On space time interest points. Int J Comput Vis 64:107–123CrossRefGoogle Scholar
  22. Lowe D (2004) Distinctive Image features from scale invariant key points. Int J Comput Vis 60:91–110CrossRefGoogle Scholar
  23. Lui L, Shao L, Li X, Lu K (2016) Learning spatio-temporal representations for action recognition: a genetic programming approach. IEEE Trans Cybern 46(1):158–170CrossRefGoogle Scholar
  24. Medjahed H, Istrate D, Boudy J, Baldinger L, Dorizzi B (2011) A pervasive multi-sensor data fusion for smart home healthcare monitoring. In: IEEE international conference on fuzzy system, pp 1466–1473Google Scholar
  25. Melfi R, Kondra S, Petrosino A (2013) Human activity modeling by spatio-temporal textural appearance. Pattern Recogn 34(15):1990–1994CrossRefGoogle Scholar
  26. Messing R, Pal C, Kautz H (2009) Activity recognition using velocity histories of tracked keypoints. In: IEEE international conference on computer vision, pp 104–111Google Scholar
  27. Piccardi M (2004) Background subtraction techniques: a review. In: IEEE international conference on systems, man and cybernetics, pp 3099–3104Google Scholar
  28. Schuldt C, Laptev I, Caputo B (2004) Recognizing human actions: a local SVM approach. Proc Int Conf Pattern Recogn 3:32–36Google Scholar
  29. Selim B, Iraqi Y, Choi HJ (2013) A multi-sensor surveillance system for elderly care. In: 15th IEEE international conference on e-health networking, applications and services, pp 502–506Google Scholar
  30. Shao L, Gao R, Lui Y, Zhang H (2011) Transform based spatio-temporal descriptor for human action recognition. Int J Neurocomput 74:962–973CrossRefGoogle Scholar
  31. Shao L, Zhen X, Tao D, Li X (2014) Spatio-temporal Laplacian pyramid coding for action recognition. IEEE Trans Cybern 44(6):817–827CrossRefGoogle Scholar
  32. Song Y, Morency LP, Davis R (2013) Action recognition by hierarchical sequence summarization. IEEE Conf Comput Vis Pattern Recogn. pp 3562–3569Google Scholar
  33. Stauffer C, Grimson WEL (2000) Learning pattern of activities using real time tracking. IEEE Trans Pattern Anal Mach Intell 22(8):747–757CrossRefGoogle Scholar
  34. Tian Y, Cao L, Liu Z, ZhangZ (2012) Hierarchical filtered motion for action recognition in crowded videos. IEEE Trans Syst Man Cybern Part C 42(3):313–323CrossRefGoogle Scholar
  35. Tsai DM, Chiu WY, Lee MH (2014) Optical motion history image (OF-MHI) for action recognition. Signal Image video Process 9(8):1897–1196CrossRefGoogle Scholar
  36. Varadarajan S, Miller P, Zhou H (2015) Region based mixture of Gaussians modeling for foreground detection in dynamic scenes. Pattern Recogn 48(11):3488–3503CrossRefzbMATHGoogle Scholar
  37. Vishwakarma DK, Singh K (2016) Human activity recognition based on spatial distribution of gradients at sub-levels of average energy silhouette images. IEEE Trans Cogn Dev Syst. Google Scholar
  38. Vishwakarma DK, Kapoor R, Dhiman A (2015) A proposed unified framework for the recognition of human activity by exploring the characteristics of action dynamics. Robot Auton Syst 77:25–38CrossRefGoogle Scholar
  39. Wang L, Tan T, Ning H, Hu W (2003) Silhouette analysis based gait recognition for human identification. IEEE Trans Pattern Anal Mach Intell 25(12):1505–1518CrossRefGoogle Scholar
  40. Wang H, Klaser A, Schmid C, Lui C-L (2011) Action recognition by dense trajectories. In: IEEE international conference on computer vision and pattern recognition, pp 3169–3176Google Scholar
  41. Wren C, Azarbayejani A, Darrell T, Pentland A (1997) Pfinder: real-time tracking of the human body. IEEE Trans Pattern Anal Mach Intell 19(7):780–785CrossRefGoogle Scholar
  42. Xu D, Lui J, Li X, Lui Z, Tang X (2005) Insignificant shadow detection for video segmentation. IEEE Trans Circ Syst Video Technol 15(8):1058–1064CrossRefGoogle Scholar
  43. Yan Y, Ricci E, Rostamzadeh N, Sebe N (2014) It’s all about habits: exploiting multi-task clustering for activities of daily living analysis. In: IEEE international conference on image processing, pp 1071–1075Google Scholar
  44. Zhang M, Sawchuk A (2013) Human daily activity recognition with spare representation using wearable sensors. IEEE J Biomed Health Inf 17(3):553–560CrossRefGoogle Scholar
  45. Zhang Z, Tao D (2012) Slow feature analysis for human action recognition. IEEE Trans Pattern Anal Mach Intell 34(3):436–450CrossRefGoogle Scholar
  46. Zhou Z, Chen X, Chung CY, He Z, Han XT, Keller JM (2008) Activity analysis, summarization, and visualization for indoor human activity monitoring. IEEE Trans Circ Syst Video Technol 18(11):1489–1498CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  • Sidharth B. Bhorge
    • 1
    Email author
  • Ramchandra R. Manthalkar
    • 2
  1. 1.Department of ElectronicsVishwakarma Institute of TechnologyPuneIndia
  2. 2.Department of E&TCShri Guru Gobind Singhji Institute of Engineering and TechnologyNandedIndia

Personalised recommendations