On-Line Selection of Discriminative Tracking Features
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Tracking Multiple Humans in Complex Situations
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Field Model for Human Detection and Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Tracking People by Learning Their Appearance
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Incremental Learning for Robust Visual Tracking
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Semi-supervised On-Line Boosting for Robust Tracking
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Robust Object Tracking by Hierarchical Association of Detection Responses
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Tracking nonstationary visual appearances by data-driven adaptation
IEEE Transactions on Image Processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition: a convolutional neural-network approach
IEEE Transactions on Neural Networks
Multi-scale convolutional neural networks for natural scene license plate detection
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part II
An adaptive sample count particle filter
Computer Vision and Image Understanding
Part template: 3D representation for multiview human pose estimation
Pattern Recognition
Neurocomputing
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In this paper, we treat tracking as a learning problem of estimating the location and the scale of an object given its previous location, scale, as well as current and previous image frames. Given a set of examples, we train convolutional neural networks (CNNs) to perform the above estimation task. Different from other learning methods, the CNNs learn both spatial and temporal features jointly from image pairs of two adjacent frames. We introduce multiple path ways in CNN to better fuse local and global information. A creative shift-variant CNN architecture is designed so as to alleviate the drift problem when the distracting objects are similar to the target in cluttered environment. Furthermore, we employ CNNs to estimate the scale through the accurate localization of some key points. These techniques are object-independent so that the proposed method can be applied to track other types of object. The capability of the tracker of handling complex situations is demonstrated in many testing sequences.