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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Walk, S. Majer, N. Schindler, K. Schiele, B. |
| Copyright Year | 2010 |
| Description | Author affiliation: Computer Science Department, TU Darmstadt (Walk, S.; Majer, N.; Schindler, K.; Schiele, B.) |
| Abstract | Despite impressive progress in people detection the performance on challenging datasets like Caltech Pedestrians or TUD-Brussels is still unsatisfactory. In this work we show that motion features derived from optic flow yield substantial improvements on image sequences, if implemented correctly — even in the case of low-quality video and consequently degraded flow fields. Furthermore, we introduce a new feature, self-similarity on color channels, which consistently improves detection performance both for static images and for video sequences, across different datasets. In combination with HOG, these two features outperform the state-of-the-art by up to 20%. Finally, we report two insights concerning detector evaluations, which apply to classifier-based object detection in general. First, we show that a commonly under-estimated detail of training, the number of bootstrapping rounds, has a drastic influence on the relative (and absolute) performance of different feature/classifier combinations. Second, we discuss important intricacies of detector evaluation and show that current benchmarking protocols lack crucial details, which can distort evaluations. |
| Starting Page | 1030 |
| Ending Page | 1037 |
| File Size | 2272397 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781424469840 |
| ISSN | 10636919 |
| e-ISBN | 9781424469857 |
| DOI | 10.1109/CVPR.2010.5540102 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-06-13 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Image motion analysis Detectors Object detection Image sequences Optical saturation Humans Histograms Cascading style sheets Feature extraction Optical sensors |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition Software |
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