On surveillance for safety critical events: In-vehicle video networks for predictive driver assistance systems

Computer Vision and Image Understanding

Volumn 134, Issue , 2015, Pages 130-140

  Ohn Bar, Eshed ^a   Tawari, Ashish ^a   Martin, Sujitha ^a   Trivedi, Mohan M ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Behavior intent analysis; Early activity recognition; Human machine interactivity; Intelligent vehicles; Multi modal sensor fusion

Indexed keywords

AUTOMOBILE DRIVERS; GESTURE RECOGNITION; IMAGE RECOGNITION; INTELLIGENT VEHICLE HIGHWAY SYSTEMS; MOTION ESTIMATION; SECURITY SYSTEMS; VEHICLES;

ACTIVITY RECOGNITION; DANGEROUS SITUATIONS; DISTRIBUTED VISION SENSORS; DRIVER ASSISTANCE SYSTEM; HEAD POSE ESTIMATION; HUMAN-MACHINE; INTENT ANALYSIS; MULTIMODAL SENSOR;

BEHAVIORAL RESEARCH;

EID: 84926144681     PISSN: 10773142     EISSN: 1090235X     Source Type: Journal    
DOI: 10.1016/j.cviu.2014.10.003     Document Type: Article

References (40)

1. B. Bhanu, C.V. Ravishankar, A.K. Roy-Chowdhury, H. Aghajan, and D. Terzopoulos Distributed Video Sensor Networks 2011 Springer
2. S. Calderara, A. Prati, and R. Cucchiara Hecol: Homography and epipolar-based consistent labeling for outdoor park surveillance Comput. Vision Image Understand. 111 2008 21 42
3. 2012 Motor Vehicle Crashes: Overview, Tech. Rep. DOT HS 811 856, National Highway Traffic Safety Administration, Washington, DC, 2013.
4. W.G. Najm, R. Ranganathan, G. Srinivasan, J.D. Smith, S. Toma, E. Swanson, A. Burgett, Description of Light-vehicle Pre-crash Scenarios for Safety Applications based on Vehicle-to-vehicle Communications, Tech. Rep. DOT HS 811 731, National Highway Traffic Safety Administration, Washington, DC, 2013.
5. P.M. Valero-Moraa, A. Tontscha, R. Welshb, A. Morrisb, S. Reedb, K. Touliouc, and D. Margaritisc Is naturalistic driving research possible with highly instrumented cars? Lessons learnt in three research centres Accid. Anal. Prev. 58 2013 187 194
6. T. Taylora, A. Pradhanb, G. Divekara, M. Romosera, J. Muttarta, R. Gomeza, A. Pollatsekc, and D. Fisherd The view from the road: the contribution of on-road glance-monitoring technologies to understanding driver behavior Accid. Anal. Prev. 58 2013 175 186
7. A Comprehensive Examination of Naturalistic Lane-changes, Tech. Rep. DOT HS 809 702, National Highway Traffic Safety Administration, Washington, DC, 2004.
8. R. Simmons, B. Browning, Y. Zhang, V. Sadekar, Learning to predict driver route and destination intent, in: IEEE Conf. Intelligent Transportation Systems, 2006.
9. S. Lefèvre, C. Laugier, J. Iba nez-Guzmán, Exploiting map information for driver intention estimation at road intersections, in: IEEE Intelligent Vehicles Symposium, 2011.
10. M. Liebner, M. Baumann, F. Klanner, C. Stiller, Driver intent inference at urban intersections using the intelligent driver model, in: IEEE Intelligent Vehicles Symposium, 2012.
11. M. Ortiz, F. Kummert, J. Schmudderich, Prediction of driver behavior on a limited sensory setting, in: IEEE Conf. Intelligent Transportation Systems, 2012.
12. V. Gadepally, A. Krishnamurthy, and Ü. Özgüner A Framework for Estimating Driver Decisions Near Intersections IEEE Trans. Intell. Transp. Syst. 15 2 2014
13. S. Lefèvre and J. Ibañez-Guzmán and C. Laugier, IEEE Symposium on Comp. Intell. Veh. Transp. Syst. (2011)
14. A. Doshi, M.M. Trivedi, Tactical driver behavior prediction and intent inference: a review, in: IEEE Conf. Intelligent Transportation Systems, 2011.
15. A. Doshi, B.T. Morris, and M.M. Trivedi On-road prediction of driver's intent with multimodal sensory cues IEEE Pervasive Comput. 10 2011 22 34
16. F. Lethaus, M.R. Baumann, F. Kster, and K. Lemmer A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data Neurocomputing 121 0 2013 108 130
17. M.-I. Toma, and D. Datcu Determining car driver interaction intent through analysis of behavior patterns Technological Innovation for Value Creation 2012 Springer 113 120
18. S. Haufe, M.S. Treder, M.F. Gugler, M. Sagebaum, G. Curio, and B. Blankertz EEG potentials predict upcoming emergency brakings during simulated driving J. Neural Eng. 8 2011 056001
19. N. Pugeault, R. Bowden, Learning pre-attentive driving behaviour from holistic visual features, in: European Conf. Computer Vision, 2010.
20. J. McCall, and M.M. Trivedi Driver behavior and situation aware brake assistance for intelligent vehicles Proc. IEEE 95 2007 374 387
21. C. Tran, A. Doshi, and M.M. Trivedi Modeling and prediction of driver behavior by foot gesture analysis Comput. Vision Image Understand. 116 2012 435 445
22. S.Y. Cheng, S. Park, and M.M. Trivedi Multi-spectral and multi-perspective video arrays for driver body tracking and activity analysis Comput. Vision Image Understand. 106 2007 245 257
23. M. Mori, C. Miyajima, P. Angkititrakul, T. Hirayama, Y. Li, N. Kitaoka, K. Takeda, Measuring driver awareness based on correlation between gaze behavior and risks of surrounding vehicles, in: IEEE Conf. Intelligent Transportation Systems, 2012.
24. H. Berndt, K. Dietmayer, Driver intention inference with vehicle onboard sensors, in: IEEE Conf. Vehicular Electronics and Safety, 2009.
25. R. Cucchiara, A. Prati, and R. Vezzani A multi-camera vision system for fall detection and alarm generation Exp. Syst. 24 2007 334 345
26. L. An, M. Kafai, and B. Bhanu Dynamic bayesian network for unconstrained face recognition in surveillance camera networks IEEE Trans. Emerg. Sel. Top. Circ. Syst. 3 2 2013 155 164
27. A. Tawari, S. Martin, and M.M. Trivedi Continuous head movement estimator (CoHMET) for driver assistance: issues, algorithms and on-road evaluations IEEE Trans. Intell. Transport. Syst. 15 2014 818 830
28. X. Xiong, F.D.I. Torre, Supervised descent method and its application to face alignment, in: IEEE Conf. Computer Vision and Pattern Recognition, 2013.
29. S. Martin, A. Tawari, E. Murphy-Chutorian, S.Y. Cheng, M. Trivedi, On the design and evaluation of robust head pose for visual user interfaces: algorithms, databases, and comparisons, in: ACM Conf. Automotive User Interfaces and Interactive Vehicular Applications, 2012.
30. E. Ohn-Bar, S. Martin, and M.M. Trivedi Driver hand activity analysis in naturalistic driving studies: issues J Electron. Imaing 22 4 2013 1 10
31. P. Dollár, R. Appel, S. Belongie, and P. Perona Fast feature pyramids for object detection IEEE Trans. Pattern Anal. Machine Intell. 36 8 2014 1532 1545
32. C. Zhang, P.A. Viola, Multiple-instance pruning for learning efficient cascade detectors, in: Advances in Neural Information Processing Systems, 2007.
33. E. Ohn-Bar and M.M. Trivedi, Beyond just keeping hands on the wheel: Towards visual interpretation of driver hand motion patterns, in: IEEE Conf. Intelligent Transportation Systems, 2014.
34. J. Bouguet, Pyramidal implementation of the affine lucas kanade feature tracker description of the algorithm. Intel Corporation (2001).
35. S. Martin, C. Tran, M. Trivedi, Optical flow based head movement and gesture analyzer (OHMeGA), in: IEEE Intl. Conf. Pattern Recognition, 2012.
36. L.P. Morency, A. Quattoni, T. Darrell, Latent-dynamic discriminative models for continuous gesture recognition, in: IEEE Conf. Computer Vision and Pattern Recognition, 2007.
37. S. Bucak, R. Jin, A.K. Jain, Multi-label multiple kernel learning by stochastic approximation: Application to visual object recognition, in: Advances in Neural Information Processing Systems, 2010.
38. A. Quattoni, S. Wang, L.-P. Morency, M. Collins, and T. Darrell Hidden conditional random fields IEEE Trans. Pattern Anal. Machine Intell. 29 2007 1848 1853
39. C.-C. Chang, and C.-J. Lin LIBSVM: a library for support vector machines ACM Trans. Intell. Syst. Technol. 2 2011 1 27
40. E. Ohn-Bar, A. Tawari, S. Martin, and M. M. Trivedi, Vision on wheels: Looking at driver, vehicle, and surround for on-road Maneuver analysis, in: IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2014.