Occlusion Robust Tracking Utilizing Spatio-Temporal Markov Random Field Model
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Robust Real-Time Face Detection
International Journal of Computer Vision
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
High-Performance Rotation Invariant Multiview Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Automatic adaptation of a generic pedestrian detector to a specific traffic scene
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
FlowBoost -- Appearance learning from sparsely annotated video
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Online domain adaptation of a pre-trained cascade of classifiers
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
| Hi-index | 0.10 |
This paper focuses on detecting vehicles in different target scenes with the same pre-trained detector which is very challenging due to view variations. To address this problem, we propose a novel approach for detection adaptation based on scene transformation, which contributes in both view transformation and automatic parameter estimation. Instead of modifying the pre-trained detectors, we transform scenes into frontal/rear view handling with pitch and yaw view variations. Without human interactions but only some general prior knowledge, the transformation parameters are automatically initialized, and then online optimized with spatial-temporal voting, which guarantees that the transformation matches the pre-trained detector. Since there is no need of labeling new samples and manual camera calibration, our approach can considerably reduce manual interactions. Experiments on challenging real-world videos demonstrate that our approach achieves significant improvements over the pre-trained detector, and it is even comparable to the performance of the detector trained on fully labeled sequences.