Multimodal highway monitoring for robust incident detection

We present detection and tracking methods for highway monitoring based on video and audio sensors, and the combination of these two modalities. We evaluate the performance of the different systems on realistic data sets that have been recorded on Austrian highways. It is shown that we can achieve a very good performance for video-based incident detection of wrong-way drivers, still standing vehicles, and traffic jams. Algorithms for simultaneous vehicle and driving direction detection using microphone arrays were evaluated and also showed a good performance on these tasks. Robust tracking in case of difficult weather conditions is achieved through multimodal sensor fusion of video and audio sensors.

[1]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[2]  Nebojsa Jojic,et al.  Audio-Video Sensor Fusion with Probabilistic Graphical Models , 2002, ECCV.

[3]  Horst Bischof,et al.  On-line Boosting and Vision , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[4]  Yoni Bauduin,et al.  Audio-Visual Speech Recognition , 2004 .

[5]  Sascha Spors,et al.  Joint audio-video object localization and tracking , 2001 .

[6]  Tuan Van Pham,et al.  Audio-Visual Feature Extraction for Semi-Automatic Annotation of Meetings , 2006, 2006 IEEE Workshop on Multimedia Signal Processing.

[7]  Ian D. Reid,et al.  A plane measuring device , 1999, Image Vis. Comput..

[8]  Horst Bischof,et al.  Robust Adaptive Classifier Grids for Object Detection from Static Cameras , 2009 .

[9]  Pascal Vasseur,et al.  Introduction to Multisensor Data Fusion , 2005, The Industrial Information Technology Handbook.

[10]  E. Mazer Inria,et al.  Using Bayesian Programming for multi-sensor multi-target tracking in automotive applications , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[11]  Carlo Tomasi,et al.  Good features to track , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[12]  Bernhard Rinner,et al.  Vehicle Classification on Multi-Sensor Smart Cameras Using Feature- and Decision-Fusion , 2007, 2007 First ACM/IEEE International Conference on Distributed Smart Cameras.

[13]  Nigel J. B. McFarlane,et al.  Segmentation and tracking of piglets in images , 1995, Machine Vision and Applications.

[14]  Jong Beom Ra,et al.  Block motion estimation based on selective integral projections , 2002, Proceedings. International Conference on Image Processing.

[15]  Pramod K. Varshney,et al.  Multisensor Data Fusion , 1997, IEA/AIE.