Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active Appearance Models Revisited
International Journal of Computer Vision
Sparse Bayesian Learning for Efficient Visual Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Online Selection of Discriminative Tracking Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Design and assessment of an intelligent activity monitoring platform
EURASIP Journal on Applied Signal Processing
Risk-sensitive loss functions for sparse multi-category classification problems
Information Sciences: an International Journal
Online adaptive radial basis function networks for robust object tracking
Computer Vision and Image Understanding
Engineering Applications of Artificial Intelligence
Manifold learning for object tracking with multiple motion dynamics
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
An efficient sequential learning algorithm for growing and pruning RBF (GAP-RBF) networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Robust online appearance models for visual tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neurocontrol of nonlinear dynamical systems with Kalman filter trained recurrent networks
IEEE Transactions on Neural Networks
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Object tracking is a fundamental computer vision problem and is required for many high-level tasks such as activity recognition, behavior analysis and surveillance. The main challenge in the object tracking problem is the dynamic change in object/background appearance, illumination, shape and occlusion. We present an online learning neural tracker (OLNT) to differentiate the object from the background and also adapt to changes in object/background dynamics. For target modeling and object tracking, a neural algorithm based on risk sensitive loss function is proposed to handle issues related to sample imbalance and dynamics of object. Region-based features like region-based color moments for larger mobile objects and color/texture features at pixel level for smaller mobile objects are used to discriminate the object from background. The proposed neural classifier automatically determines the number of neurons required to estimate the posterior probability map. In the online learning neural classifier, only one neuron parameter is updated per tracker to reduce the computational burden during online adaptation. The tracked object is represented using an estimated posterior probability map. The posterior probability map is used to adapt the bounding box to handle the scale change and improper initialization. For illustrating the advantage of the proposed OLNT under rapid illumination variation, change in appearance, scale/size change, and occlusion, we present results from benchmark video sequences. Finally, we also present the comparison with well-known trackers in the literature and highlight the advantage of the proposed tracker.