Learning features for activity recognition with shift-invariant sparse coding

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 8131 LNCS, Issue , 2013, Pages 367-374

  Vollmer, Christian ^a   Gross, Horst Michael ^a   Eggert, Julian P ^b  

^a ILMENAU UNIVERSITY OF TECHNOLOGY   (Germany)

^b HONDA RESEARCH INSTITUTE EUROPE GMBH   (Germany)

Author keywords

activity recognition; feature learning; sparse coding

Indexed keywords

ACTIVITY RECOGNITION; CONVENTIONAL APPROACH; DEEP BELIEF NETWORKS; FEATURE LEARNING; SPARSE CODING; STANDARD DEVIATION; STATISTICAL MEASURES; TRADITIONAL TECHNIQUES;

CLASSIFICATION (OF INFORMATION); LEARNING ALGORITHMS; LEARNING SYSTEMS; NEURAL NETWORKS; PRINCIPAL COMPONENT ANALYSIS;

PATTERN RECOGNITION;

EID: 84884934410     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-40728-4_46     Document Type: Conference Paper

References (11)

1. Eggert, J., Wersing, H., Korner, E.: Transformation-invariant representation and NMF. In: Proc. of the 2004 IEEE Int. Joint Conf. on Neural Networks, vol. 4, pp. 2535-2539. IEEE (2004)
2. Huynh, T., Schiele, B.: Analyzing features for activity recognition. In: Proc. of the 2005 Joint Conf. on Smart Objects and Ambient Intelligence (2005)
3. Huynh, T., Fritz, M., Schiele, B.: Discovery of activity patterns using topic models. In: Proc. of the 10th Int. Conf. on Ubiquitous Computing, pp. 10-19. ACM Press, New York (2008)
4. Kim, T., Shakhnarovich, G., Urtasun, R.: Sparse Coding for Learning Interpretable Spatio-Temporal Primitives. In: Proc. Neural Inf. Process. Syst (NIPS), vol. 22 (December 2010)
5. Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature 401(6755), 788-791 (1999)
6. Pham, C., Olivier, P.: Slice&Dice: Recognizing food preparation activities using embedded accelerometers. In: Tscheligi, M., de Ruyter, B., Markopoulus, P., Wichert, R., Mirlacher, T., Meschterjakov, A., Reitberger, W. (eds.) AmI 2009. LNCS, vol. 5859, pp. 34-43. Springer, Heidelberg (2009)
7. Plötz, T., Hammerla, N.Y., Olivier, P.: Feature Learning for Activity Recognition in Ubiquitous Computing. In: Proc. of the Twenty-Second Int. Joint Conf. on Artificial Intelligence, pp. 1729-1734 (2011)
8. Roggen, D., et al.: Collecting complex activity datasets in highly rich networked sensor environments. In: 2010 Seventh Int. Conf. on Networked Sensing Systems (INSS), pp. 233-240 (June 2010)
9. Smith, E., Lewicki, M.S.: Efficient coding of time-relative structure using spikes. Neural Computation 17(1), 19-45 (2005)
10. Vollmer, C., Eggert, J.P., Groß, H.-M.: Generating Motion Trajectories by Sparse Activation of Learned Motion Primitives. In: Villa, A.E.P., Duch, W., Érdi, P., Masulli, F., Palm, G. (eds.) ICANN 2012, Part I. LNCS, vol. 7552, pp. 637-644. Springer, Heidelberg (2012)
11. Zappi, P., Lombriser, C., Stiefmeier, T., Farella, E., Roggen, D., Benini, L., Tröster, G.: Activity recognition from on-body sensors: Accuracy-power trade-off by dynamic sensor selection. In: Verdone, R. (ed.) EWSN 2008. LNCS, vol. 4913, pp. 17-33. Springer, Heidelberg (2008)