Fast feature pyramids for object detection
Research output: Contribution to journal › Journal article › Research › peer-review
Multi-resolution image features may be approximated via extrapolation from nearby scales, rather than being computed explicitly. This fundamental insight allows us to design object detection algorithms that are as accurate, and considerably faster, than the state-of-the-art. The computational bottleneck of many modern detectors is the computation of features at every scale of a finely-sampled image pyramid. Our key insight is that one may compute finely sampled feature pyramids at a fraction of the cost, without sacrificing performance: for a broad family of features we find that features computed at octave-spaced scale intervals are sufficient to approximate features on a finely-sampled pyramid. Extrapolation is inexpensive as compared to direct feature computation. As a result, our approximation yields considerable speedups with negligible loss in detection accuracy. We modify three diverse visual recognition systems to use fast feature pyramids and show results on both pedestrian detection (measured on the Caltech, INRIA, TUD-Brussels and ETH data sets) and general object detection (measured on the PASCAL VOC). The approach is general and is widely applicable to vision algorithms requiring fine-grained multi-scale analysis. Our approximation is valid for images with broad spectra (most natural images) and fails for images with narrow band-pass spectra (e.g., periodic textures).
Original language | English |
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Article number | 6714453 |
Journal | IEEE Transactions on Pattern Analysis and Machine Intelligence |
Volume | 36 |
Issue number | 8 |
Pages (from-to) | 1532-1545 |
Number of pages | 14 |
ISSN | 0162-8828 |
DOIs | |
Publication status | Published - Aug 2014 |
Externally published | Yes |
- image pyramids, natural image statistics, object detection, pedestrian detection, real-time systems, Visual features
Research areas
ID: 302045927