Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
W4: Real-Time Surveillance of People and Their Activities
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Introduction to the Kalman Filter
An Introduction to the Kalman Filter
Object Tracking in Video Sequences by Unsupervised Learning
CAIP '09 Proceedings of the 13th International Conference on Computer Analysis of Images and Patterns
Tuning of the structure and parameters of a neural network using an improved genetic algorithm
IEEE Transactions on Neural Networks
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Object tracking in video sequences remains as one of the most challenging problems in computer vision. Object occlusion, sudden trajectory changes and other difficulties still wait for comprehensive solutions. Here we propose a feature weighting method which is able to discover the most relevant features for this task, and a competitive learning neural network which takes advantage of such information in order to produce consistent estimates of the trajectories of the objects. The feature weighting is done with the help of a genetic algorithm, and each unit of the neural network remembers its past history so that sudden movements are adequately accounted for. Computational experiments with real and artificial data demonstrate the performance of the proposed system when compared to the standard Kalman filter.