Effective Comparison Features for Pedestrian Detection
SCIE
SCOPUS
- Title
- Effective Comparison Features for Pedestrian Detection
- Authors
- Kong, KK; Lee, JW; Hong, KS
- Date Issued
- 2016-07
- Publisher
- SPRINGER INT PUBLISHING AG
- Abstract
- For real applications of pedestrian detection, both detection speed and detection accuracy are important. In this paper we propose a detector based on effective comparison features (ECFs) for simultaneously improving detection accuracy and speed. ECFs are defined as the features helping to improve actual performance. Using only these ECFs as feature candidates for the split nodes of decision trees, our detector can achieve accurate results. As an additional benefit, detection speed is improved by earlier rejection of negative samples. Experiments are conducted using well-known benchmark datasets for pedestrian detection. The experimental results of our ECF detector show that our detection speed is 1-2 orders of magnitude faster than the speed of state-of-the-art algorithms, with comparable detection accuracy.
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/37495
- DOI
- 10.1007/978-3-319-41501-7_34
- ISSN
- 0302-9743
- Article Type
- Article
- Citation
- Lecture Notes in Computer Science, vol. 9730, page. 299 - 308, 2016-07
- Files in This Item:
- There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.