Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/90785
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Type: | Conference paper |
Title: | Strengthening the effectiveness of pedestrian detection with spatially pooled features |
Author: | Paisitkriangkrai, S. Shen, C. Van Den Hengel, A. |
Citation: | Lecture Notes in Artificial Intelligence, 2014 / Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (ed./s), vol.8692 LNCS, iss.PART 4, pp.546-561 |
Publisher: | Springer International Publishing |
Issue Date: | 2014 |
Series/Report no.: | Lecture Notes in Computer Science; 8692 |
ISBN: | 9783319105925 |
ISSN: | 0302-9743 1611-3349 |
Conference Name: | 13th European Conference on Computer Vision (ECCV 2014) (6 Sep 2014 - 12 Sep 2014 : Zurich, Switzerland) |
Editor: | Fleet, D. Pajdla, T. Schiele, B. Tuytelaars, T. |
Statement of Responsibility: | Sakrapee Paisitkriangkrai, Chunhua Shen, Anton van den Hengel |
Abstract: | We propose a simple yet effective approach to the problem of pedestrian detection which outperforms the current state-of-the-art. Our new features are built on the basis of low-level visual features and spatial pooling. Incorporating spatial pooling improves the translational invariance and thus the robustness of the detection process. We then directly optimise the partial area under the ROC curve (pAUC) measure, which concentrates detection performance in the range of most practical importance. The combination of these factors leads to a pedestrian detector which outperforms all competitors on all of the standard benchmark datasets. We advance state-of-the-art results by lowering the average miss rate from 13% to 11% on the INRIA benchmark, 41% to 37% on the ETH benchmark, 51% to 42% on the TUD-Brussels benchmark and 36% to 29% on the Caltech-USA benchmark. |
Rights: | ©Springer International Publishing Switzerland 2014 |
DOI: | 10.1007/978-3-319-10593-2_36 |
Published version: | http://dx.doi.org/10.1007/978-3-319-10593-2_36 |
Appears in Collections: | Aurora harvest 7 Computer Science publications |
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