Automatic workflow monitoring in industrial environments

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

Volumn 6492 LNCS, Issue PART 1, 2011, Pages 200-213

  Veres, Galina ^a   Grabner, Helmut ^b   Middleton, Lee ^a   Van Gool, Luc ^b,c  

^a UNIVERSITY OF SOUTHAMPTON   (United Kingdom)

^b ETH ZURICH   (Switzerland)

^c UNIVERSITY OF LEUVEN   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

CAR MANUFACTURING; DATA SETS; DESCRIPTORS; ENVIRONMENTAL CONDITIONS; INDUSTRIAL ENVIRONMENTS; LARGE OCCLUSION; RECORDING DATA; ROBUST APPROACHES; SAFETY REQUIREMENTS; SERIES ANALYSIS; UNSOLVED PROBLEMS; VISUAL SENSOR; WORKFLOW MONITORING;

AUTOMOBILE MANUFACTURE; COMPUTER VISION; INDUSTRY; MACHINERY; TIME SERIES;

TIME SERIES ANALYSIS;

EID: 79952525554     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-19315-6_16     Document Type: Conference Paper

References (19)

1. Adam, A., Rivlin, E., Shimshoni, I., Reinitz, D.: Robust real-time unusual event detection using multiple fixed-location monitors. PAMI 30, 555-560 (2008) (Pubitemid 351280062)
2. Boiman, O., Irani, M.: Detecting irregularities in images and in video. In: Proc. ICCV (2005)
3. Breitenstein, M., Grabner, H., Gool, L.V.: Hunting nessie: Real time abnormality detection from webcams. In: Proc. ICCV Workshop on Visual Survaillance (2009)
4. Felzenszwalb, P., McAllester, D., Ramanan, D.: A discriminatively trained, multiscale, deformable part model. In: Proc. CVPR (2008)
5. Grabner, H., Bischof, H.: On-line boosting and vision. In: Proc. CVPR, vol. 1, pp. 260-267 (2006) (Pubitemid 44931339)
6. Jaeger, H.: The "echo state" approach to analysing and training recurrent neural networks. Technical Report GMD Report 148, German National Research Institute for Computer Science (2001)
7. Jaeger, H.: Adaptive nonlinear system identification with echo state networks. In: Proc. NIPS, vol. 15, pp. 593-600 (2003)
8. Johnson, N., Hogg, D.: Learning the distribution of object trajectories for event recognition. In: Proc. BMVC (1996)
9. Huang, C., Wu, D., Nevatia, R.: Robust Object Tracking by Hierarchical Association of Detection Responses. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 788-801. Springer, Heidelberg (2008)
10. Li, J., Gong, S., Xiang, T.: Scene segmentation for behaviour correlation. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part IV. LNCS, vol. 5305, pp. 383-395. Springer, Heidelberg (2008)
11. Makris, D., Ellis, T.: Learning semantic scene models from observing activity in visual surveillance. Trans. on Systems, Man, and Cybernetics 35, 397-408 (2005) (Pubitemid 40802755)
12. Lv, F., Nevatia, R.: Recognition and segmentation of 3-D human action using HMM and multi-class adaBoost. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3954, pp. 359-372. Springer, Heidelberg (2006) (Pubitemid 44029643)
13. Ozturk, M.C., Xu, D., Principe, J.C.: Analysis and design of echo state networks. Neural Computation 19, 111-138 (2007)
14. Padoy, N., Mateus, D., Weinland, D., Berger, M., Navab, N.: Workflow monitoring based on 3d motion features. In: Proc. ICCV/WS on Video-oriented Object and Event Classification (2009)
15. Scherer, S., Oubbati, M., Schwenker, F., Palm, G.: Real-time emotion recognition from speech using echo state networks. Artificial Intelligence, 205-216 (2008)
16. Skowronski, M., Harris, J.: Automatic speech recognition using a predictive echo state network classifier. Neural Networks 20, 414-423 (2007) (Pubitemid 46863403)
17. Stauffer, C., Grimson, W.: Adaptive background mixture models for real-time tracking. In: Proc. CVPR, vol. 2, pp. 246-252 (1999)
18. Wang, X., Ma, K., Ng, G., Grimson, W.: Trajectory analysis and semantic region modeling using a nonparametric bayesian model. In: Proc. CVPR (2008)
19. Zhong, H., Shi, J., Visontai, M.: Detecting unusual activity in video. In: Proc. CVPR (2004)