On-Line Selection of Discriminative Tracking Features
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
IEEE Transactions on Pattern Analysis and Machine Intelligence
Randomized Trees for Real-Time Keypoint Recognition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
A fast learning algorithm for deep belief nets
Neural Computation
Semi-supervised On-Line Boosting for Robust Tracking
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Online Tracking and Reacquisition Using Co-trained Generative and Discriminative Trackers
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
IEEE Transactions on Neural Networks
Globally optimal solution to multi-object tracking with merged measurements
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
An adaptive coupled-layer visual model for robust visual tracking
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Hand gesture recognition with depth data
Proceedings of the 4th ACM/IEEE international workshop on Analysis and retrieval of tracked events and motion in imagery stream
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Currently, object tracking/detection is based on a "shallow learning" paradigm; they locally process features to build an object model and then they apply adaptive methodologies to estimate model parameters. However, such an approach presents the drawback of losing the "whole picture information" required to maintain a stable tracking for long time and high visual changes. To overcome these obstacles, we need a "deep" information fusion framework. Deep learning is a new emerging research area that simulates the efficiency and robustness by which the humans' brain represents information; it deeply propagates data into complex hierarchies. However, implementing a deep fusion learning paradigm in a machine presents research challenges mainly due to the highly non-linear structures involved and the "curse of dimensionality". Another difficulty which is critical in computer vision applications is that learning should be self adapted to guarantee stable object detection over long time spans. In this paper, we propose a novel fast (in real-time) and adaptive information fusion strategy that exploits the deep learning paradigm. The proposed framework integrates optimization strategies able to update in real-time the non-linear model parameters according in a way to trust, as much as possible, the current changes of the environment, while providing a minimal degradation of the previous gained experience.