A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pedestrian Detection in Crowded Scenes
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Viewpoint Invariant Approach for Crowd Counting
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Gaussian fields for semi-supervised regression and correspondence learning
Pattern Recognition
Estimating crowd density with Minkowski fractal dimension
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 06
Machine Vision and Applications
Kernel regression with order preferences
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Semi-supervised regression with co-training
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Multiple view semi-supervised dimensionality reduction
Pattern Recognition
Self-adaptive local Fisher discriminant analysis for semi-supervised image recognition
International Journal of Biometrics
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Pedestrian counting plays an important role in public safety and intelligent transportation. Most pedestrian counting algorithms based on supervised learning require much labeling work and rarely exploit the topological information of unlabelled data in a video. In this paper, we propose a Semi-Supervised Elastic Net (SSEN) regression method by utilizing sequential information between unlabelled samples and their temporally neighboring samples as a regularization term. Compared with a state-of-the-art algorithm, extensive experiments indicate that our algorithm can not only select sparse representative features from the original feature space without losing their interpretability, but also attain superior prediction performance with only very few labelled frames.