The capability of estimating the walking direction of people would be useful in many applications such as those involving autonomous cars and robots.We introduce an approach for estimating the walking direction of people from images, based on learning the correct classification of a still image by using SVMs. We find that the performance of the system can be improved by classifying each image of a walking sequence and combining the outputs of the classifier.Experiments were performed to evaluate our system and estimate the trade-off between number of images in walking sequences and performance.
| Identifer | oai:union.ndltd.org:MIT/oai:dspace.mit.edu:1721.1/30397 |
| Date | 27 August 2003 |
| Creators | Shimizu, Hiroaki, Poggio, Tomaso |
| Source Sets | M.I.T. Theses and Dissertation |
| Language | en_US |
| Detected Language | English |
| Format | 11 p., 12026063 bytes, 489901 bytes, application/postscript, application/pdf |
| Relation | Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory |
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