Fast Human Detection Using a Novel Boosted Cascading Structure With Meta Stages
- 15 July 2008
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Image Processing
- Vol. 17 (8), 1452-1464
- https://doi.org/10.1109/tip.2008.926152
Abstract
We propose a method that can detect humans in a single image based on a novel cascaded structure. In our approach, both intensity-based rectangle features and gradient-based 1-D features are employed in the feature pool for weak-learner selection. The Real AdaBoost algorithm is used to select critical features from a combined feature set and learn the classifiers from the training images for each stage of the cascaded structure. Instead of using the standard boosted cascade, the proposed method employs a novel cascaded structure that exploits both the stage-wise classification information and the interstage cross-reference information. We introduce meta-stages to enhance the detection performance of a boosted cascade. Experiment results show that the proposed approach achieves high detection accuracy and efficiency.Keywords
This publication has 37 references indexed in Scilit:
- Pedestrian Detection using Infrared images and Histograms of Oriented GradientsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2006
- On-line Boosting and VisionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2006
- Multi-Aspect Detection of Articulated ObjectsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2006
- Rapid object detection using a boosted cascade of simple featuresPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Integral histogram: a fast way to extract histograms in Cartesian spacesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Pedestrian detection for driving assistance systems: single-frame classification and system level performancePublished by Institute of Electrical and Electronics Engineers (IEEE) ,2004
- Human detection using geometrical pixel value structuresPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Detecting pedestrians using patterns of motion and appearancePublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Improved Boosting Algorithms Using Confidence-rated PredictionsMachine Learning, 1999
- Example-based learning for view-based human face detectionIEEE Transactions on Pattern Analysis and Machine Intelligence, 1998