Object detection using discriminative photogrammetric context

Photogrammetric context captures the relationship between object heights and camera viewpoint, and can be used to reject false detections that appear in wrong locations or scales. In this work, we address the problem of using photogrammetric constraints in object detection when camera poses are unkn...

Full description

Saved in:
Bibliographic Details
Main Authors: Yuanliu Liu, Yang Wu, Zejian Yuan
Format: Conference Proceeding
Language:English
Subjects:
Branch-bound-and-cut
Cameras
Context
Context modeling
Detectors
Estimation
Feature extraction
Latent Structural SVM
Object detection
Photogrammetric context
Training
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Photogrammetric context captures the relationship between object heights and camera viewpoint, and can be used to reject false detections that appear in wrong locations or scales. In this work, we address the problem of using photogrammetric constraints in object detection when camera poses are unknown. We propose a model to capture both local appearance features and global photogrammetric context, in which the camera pose is treated as a latent variable. We use latent Structural SVM to learn the model parameters. To solve the NP-hard problem in structured prediction, we propose a branch-bound-and-cut algorithm, where cuts of the latent variable are embedded into a branch-and-bound process. The model is experimentally evaluated on INRIA pedestrian dataset. The results show that our model can get significantly better detection performance than models using only appearance features or using photogrammetric context in a graphical model.
ISSN:1522-4880
2381-8549
DOI:10.1109/ICIP.2011.6116127