Will the pedestrian cross? Probabilistic path prediction based on learned motion features

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

Volumn 6835 LNCS, Issue , 2011, Pages 386-395

  Keller, Christoph G ^a   Hermes, Christoph ^b   Gavrila, Dariu M ^c,d  

^a UNIVERSITY OF HEIDELBERG   (Germany)

^b BIELEFELD UNIVERSITY   (Germany)

^c DAIMLER AG   (Germany)

^d UNIVERSITY OF AMSTERDAM   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURATE ANALYSIS; CLASSIFICATION TASKS; DENSE OPTICAL FLOW; FILTERING FRAMEWORK; HUMAN OBSERVERS; HUMAN PERFORMANCE; MOTION FEATURES; PATH PREDICTION; RECOGNITION PERFORMANCE; SYSTEM VARIANTS; TIME INTERVAL; TRAFFIC SCENE; TRAFFIC SITUATIONS; TRAJECTORY MATCHING; VEHICLE SYSTEM; VEHICLE-BASED SYSTEMS;

PATTERN RECOGNITION SYSTEMS;

PEDESTRIAN SAFETY;

EID: 80053005685     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-23123-0_39     Document Type: Conference Paper

References (20)

1. Abramson, Y., Steux, B.: Hardware-friendly pedestrian detection and impact prediction. In: IEEE Intell. Veh., pp. 590-595 (2004)
2. Antonini, G., Martinez, S.V., Bierlaire, M., Thiran, J.: Behavioral priors for detection and tracking of pedestrians in video sequences. IJCV 69(2) (2006)
3. Bar-Shalom, Y., Li, X., Kirubarajan, T.: Estimation with applications to tracking and navigation. Wiley-Interscience, Hoboken (2001)
4. Black, M.J., Jepson, A.D.: A probabilistic framework for matching temporal trajectories: CONDENSATION-based recognition of gestures and expressions. In: Burkhardt, H.-J., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1406, pp. 909-924. Springer, Heidelberg (1998)
5. Chen, Z., Ngai, D., Yung, N.: Pedestrian behavior prediction based on motion patterns for vehicle-to-pedestrian collision avoidance. In: Proc. of IEEE ITSC, pp. 316-321 (2008)
6. Comaniciu, D., Meer, P.: Mean shift: A robust approach toward feature space analysis. IEEE PAMI 24(5), 603-619 (2002)
7. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proc. CVPR, pp. 886-893 (2005)
8. De Nicolao, G., Ferrara, A., Giacomini, L.: A collision risk assessment approach as a basis for the on-board warning generation in cars. In: IEEE Intell. Veh. (2002)
9. Dollar, P., Wojek, C., Schiele, B., Perona, P.: Pedestrian detection: A benchmark. In: Proc. CVPR (2009)
10. Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE PAMI 31, 2179-2195 (2009)
11. Hirschmüller, H.: Stereo processing by semiglobal matching and mutual information. IEEE PAMI 30(2), 328-341 (2008)
12. Howe, N., Leventon, M., Freeman, W.: Bayesian reconstruction of 3d human motion from single-camera video. In: Proc. NIPS, pp. 820-826 (2000)
13. Käfer, E., Hermes, C., Wöhler, C., Ritter, H., Kummert, F.: Recognition of situation classes at road intersections. In: Proc. ICRA, pp. 3960-3965 (2010)
14. Makris, D., Ellis, T.: Spatial and probabilistic modelling of pedestrian behaviour. In: Proc. BMVC, pp. 557-566 (2002)
15. Meinecke, M.M., et al.: Strategies in terms of vulnerable road user protection. EU Project SAVE-U, Deliverable D6 (2003), http://www.save-u.org
16. Schmidt, S., Färber, B.: Pedestrians at the kerb - recognising the action intentions of humans. Transportation Research Part F 12(4), 300-310 (2009)
17. Sidenbladh, H., Black, M.J., Sigal, L.: Implicit probabilistic models of human motion for synthesis and tracking. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 784-800. Springer, Heidelberg (2002)
18. Turaga, P.K., Chellappa, R., Subrahmanian, V.S., Udrea, O.: Machine recognition of human activities: A survey. CirSysVideo 18(11), 1473-1488 (2008)
19. Wakim, C., Capperon, S., Oksman, J.: A Markovian model of pedestrian behavior. In: Proc. IEEE Int. Conf. Syst., Man, Cybern., pp. 4028-4033 (2004)
20. Wedel, A., et al.: Duality tv-l1 flow with fundamental matrix prior. In: Proc. IVCNZ (2008)