Multi-Aspect Detection of Articulated Objects
- 1 January 2006
- proceedings article
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 2, 1582-1588
- https://doi.org/10.1109/cvpr.2006.193
Abstract
A wide range of methods have been proposed to detect and recognize objects. However, effective and efficient multiviewpoint detection of objects is still in its infancy, since most current approaches can only handle single viewpoints or aspects. This paper proposes a general approach for multiaspect detection of objects. As the running example for detection we use pedestrians, which add another difficulty to the problem, namely human body articulations. Global appearance changes caused by different articulations and viewpoints of pedestrians are handled in a unified manner by a generalization of the Implicit Shape Model [5]. An important property of this new approach is to share local appearance across different articulations and viewpoints, therefore requiring relatively few training samples. The effectiveness of the approach is shown and compared to previous approaches on two datasets containing pedestrians with different articulations and from multiple viewpoints.Keywords
This publication has 11 references indexed in Scilit:
- Histograms of Oriented Gradients for Human DetectionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Pedestrian Detection in Crowded ScenesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- An Evaluation of Local Shape-Based Features for Pedestrian DetectionPublished by British Machine Vision Association and Society for Pattern Recognition ,2005
- Detection of multiple, partially occluded humans in a single image by Bayesian combination of edgelet part detectorsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Adaptive background mixture models for real-time trackingPublished 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
- A statistical method for 3D object detection applied to faces and carsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Shape matching and object recognition using shape contextsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Example-based object detection in images by componentsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2001
- A Trainable System for Object DetectionInternational Journal of Computer Vision, 2000