Tracking peoples movement is an extremely time consuming task to do by hand however such analysis informs our ability to realistically model peoples moment in pedestrian models.Researchers at the Computer Vision Laboratory, ETH have been working on multi-object tracking which combines object detection and space-time trajectory estimation. The ability to automate such a process obviously has benefits for understanding pedestrian movement in cities
To see a video of the work click here.
References
B. Leibe, K. Schindler, N. Cornelis, L. Van Gool. (2008). Coupled Object Detection and Tracking from Static Cameras and Moving Vehicles. In IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 30, No. 10, pp. 1683-1698.
A. Ess, B. Leibe, K. Schindler, L. Van Gool. (2008). A Mobile Vision System for Robust Multi-Person Tracking. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR'08), Anchorage, USA, June 2008.
Digital Urban pedestrian tracking tags
A. Ess, B. Leibe, K. Schindler, L. Van Gool. (2008). A Mobile Vision System for Robust Multi-Person Tracking. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR'08), Anchorage, USA, June 2008.
Digital Urban pedestrian tracking tags
1 comment:
the video looks impressive! if the camera was in a fixed position this could be coupled with GIS.
cant wait to see further development;)
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