Smart Automotive Airbags: Occupant Classification and Tracking

The introduction of airbags into automobiles has significantly improved the safety of the occupants. Unfortunately, airbags can also cause fatal injuries if the occupant is a child smaller (in weight) than a typical six-year-old. Between 1986 and 2001, 19 infants and 85 children were killed by airbags during relatively minor vehicle collisions. In addition to these infant and child deaths, there have also been seven adults killed by airbags due to their proximity to the airbag during deployment. In response to these deaths, the National Highway Transportation and Safety Administration has mandated that, starting in the 2006 model year, all automobiles be equipped with automatic airbag suppression. The suppression of the airbag based on the type of occupant can be framed as a two-class classification problem, while the suppression of the airbag based on the location of the occupant relative to the airbag can be framed as an occupant-tracking problem. This paper describes an integrated real-time vision-based occupant classification and tracking system using a single grayscale camera with commercially available processing hardware. The classification system has achieved a classification accuracy of approximately 98%. Likewise, the tracking system has demonstrated the ability to detect a dangerous proximity of the occupant relative to the airbag within only 7 ms

[1]  Anil K. Jain,et al.  Interacting multiple model (IMM) Kalman filters for robust high speed human motion tracking , 2002, Object recognition supported by user interaction for service robots.

[2]  Alex Pentland,et al.  A Bayesian Computer Vision System for Modeling Human Interactions , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Steven S. Beauchemin,et al.  The computation of optical flow , 1995, CSUR.

[4]  Daphne Koller,et al.  Toward Optimal Feature Selection , 1996, ICML.

[5]  Larry S. Davis,et al.  W4: Real-Time Surveillance of People and Their Activities , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Rainer Wegenkittl,et al.  Implementation and Complexity of the Watershed‐from‐Markers Algorithm Computed as a Minimal Cost Forest , 2001, Comput. Graph. Forum.

[7]  John Krumm,et al.  Video occupant detection for airbag deployment , 1998, Proceedings Fourth IEEE Workshop on Applications of Computer Vision. WACV'98 (Cat. No.98EX201).

[8]  Andrew W. Fitzgibbon,et al.  Ellipse-specific direct least-square fitting , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[9]  김석일,et al.  Vehicle seat weight sensor , 2006 .

[10]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[11]  Huan Liu,et al.  Feature Selection for Classification , 1997, Intell. Data Anal..

[12]  Mikko Pekkarinen,et al.  Multiple model approaches to multisensor tracking , 2000 .

[13]  Brian K Blackburn,et al.  Apparatus and method for controlling occupant restraint system , 1994 .

[14]  Jos B. T. M. Roerdink,et al.  The Watershed Transform: Definitions, Algorithms and Parallelization Strategies , 2000, Fundam. Informaticae.

[15]  Junji Yamato,et al.  Recognizing human action in time-sequential images using hidden Markov model , 1992, Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[16]  Andrew Blake,et al.  Tracking through singularities and discontinuities by random sampling , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[17]  (54) AUTOMOTIVE OCCUPANT SENSOR SYSTEM AND METHOD OF OPERATION BY SENSOR FUSION SENSORSYSTEM ZUR ERFASSUNG VON FAHRZEUGINSASSEN UND BETRIEBSVERFAHREN UNTER VERWENDUNG VON FUSIONIERTEN SENSORINFORMATIONEN SYSTEME DE DETECTION D’OCCUPATION D’UNE AUTOMOBILE ET PROCEDE PERMETTANT SON FONCTIONNEMENT FAIS , 2022 .

[18]  R. Mukundan,et al.  Moment Functions in Image Analysis: Theory and Applications , 1998 .

[19]  Mineichi Kudo,et al.  Comparison of algorithms that select features for pattern classifiers , 2000, Pattern Recognit..

[20]  Anil K. Jain,et al.  Feature extraction methods for character recognition-A survey , 1996, Pattern Recognit..

[21]  Linda G. Shapiro,et al.  Computer Vision , 2001 .

[22]  Daniel P. Huttenlocher,et al.  Comparing Images Using the Hausdorff Distance , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[23]  Y. Bar-Shalom,et al.  The interacting multiple model algorithm for systems with Markovian switching coefficients , 1988 .

[24]  Sim Heng Ong,et al.  Image Analysis by Tchebichef Moments , 2001, IEEE Trans. Image Process..

[25]  Jake K. Aggarwal,et al.  Human Motion Analysis: A Review , 1999, Comput. Vis. Image Underst..

[26]  Arthur Gelb,et al.  Applied Optimal Estimation , 1974 .

[27]  Anil K. Jain,et al.  Occupant classification system for automotive airbag suppression , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[28]  Robert P. W. Duin,et al.  A note on comparing classifiers , 1996, Pattern Recognit. Lett..

[29]  Sven Loncaric,et al.  A survey of shape analysis techniques , 1998, Pattern Recognit..

[30]  Swarup Medasani,et al.  Vision-based fusion system for smart airbag applications , 2002, Intelligent Vehicle Symposium, 2002. IEEE.

[31]  Anil K. Jain,et al.  Segmentation, classification, and tracking of humans for smart airbag applications , 2004 .

[32]  M. Teague Image analysis via the general theory of moments , 1980 .

[33]  R. Singer Estimating Optimal Tracking Filter Performance for Manned Maneuvering Targets , 1970, IEEE Transactions on Aerospace and Electronic Systems.

[34]  David W. Aha,et al.  A Comparative Evaluation of Sequential Feature Selection Algorithms , 1995, AISTATS.

[35]  Katsuhiko Sakaue,et al.  Head pose estimation by nonlinear manifold learning , 2004, ICPR 2004.

[36]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[37]  Chitra Dorai,et al.  Practicing vision: Integration, evaluation and applications , 1997, Pattern Recognit..

[38]  Christoph Bregler,et al.  Learning and recognizing human dynamics in video sequences , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[39]  Amir Averbuch,et al.  Interacting Multiple Model Methods in Target Tracking: A Survey , 1988 .

[40]  Kentaro Toyama,et al.  Wallflower: principles and practice of background maintenance , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[41]  R. Larsen An introduction to mathematical statistics and its applications / Richard J. Larsen, Morris L. Marx , 1986 .

[42]  Remco C. Veltkamp,et al.  State of the Art in Shape Matching , 2001, Principles of Visual Information Retrieval.

[43]  Anil K. Jain,et al.  Statistical Pattern Recognition: A Review , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[44]  Andrew Blake,et al.  Classification of human body motion , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[45]  Jinling Wang,et al.  An Extended Dynamic Model for Kinematic Positioning , 2003, Journal of Navigation.

[46]  Sankar K. Pal,et al.  A review on image segmentation techniques , 1993, Pattern Recognit..

[47]  Qin Lu,et al.  Recognition of Chinese Characters by Moment Feature Extraction , 1997 .

[48]  Alex Pentland,et al.  Pfinder: real-time tracking of the human body , 1996, Proceedings of the Second International Conference on Automatic Face and Gesture Recognition.

[49]  Ludwig Listl,et al.  Fast range imaging by CMOS sensor array through multiple double short time integration (MDSI) , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[50]  Michael Isard,et al.  CONDENSATION—Conditional Density Propagation for Visual Tracking , 1998, International Journal of Computer Vision.

[51]  Greg Welch,et al.  Welch & Bishop , An Introduction to the Kalman Filter 2 1 The Discrete Kalman Filter In 1960 , 1994 .

[52]  Anil K. Jain,et al.  Integrated segmentation and classification for automotive airbag suppression , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[53]  Cyrus Shahabi,et al.  Image retrieval by shape: a comparative study , 2000, 2000 IEEE International Conference on Multimedia and Expo. ICME2000. Proceedings. Latest Advances in the Fast Changing World of Multimedia (Cat. No.00TH8532).