Excessive Noise Injection Training of Neural Networks for Markerless Tracking in Obscured and Segmented Environments.

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Bibliographic Details
Title:	Excessive Noise Injection Training of Neural Networks for Markerless Tracking in Obscured and Segmented Environments.
Authors:	Unsworth, C. P., Coghill, G.
Source:	Neural Computation. Sep2006, Vol. 18 Issue 9, p2122-2145. 24p.
Subjects:	Artificial neural networks, Error detection & recovery in robotics, Blocking (Motion pictures), Real-time control, Optical images, Object monitors (Computer software), Extension (Logic)
Abstract:	In this letter, we demonstrate that the generalization properties of a neural network (NN) can be extended to encompass objects that obscure or segment the original image in its foreground or background. We achieve this by piloting an extension of the noise injection training technique, which we term excessive noise injection (ENI), on a simple feedforward multilayer perceptron (MLP) network with vanilla backward error propagation to achieve this aim. Six tests are reported that show the ability of an NN to distinguish six similar states of motion of a simplified human figure that has become obscured by moving vertical and horizontal bars and random blocks for different levels of obscuration. Four more extensive tests are then reported to determine the bounds of the technique. The results from the ENI network were compared to results from the same NN trained on clean states only. The results pilot strong evidence that it is possible to track a human subject behind objects using this technique, and thus this technique lends itself to a real-time markerless tracking system from a single video stream. [ABSTRACT FROM AUTHOR]
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Database:	Psychology and Behavioral Sciences Collection

Description
Abstract:	In this letter, we demonstrate that the generalization properties of a neural network (NN) can be extended to encompass objects that obscure or segment the original image in its foreground or background. We achieve this by piloting an extension of the noise injection training technique, which we term excessive noise injection (ENI), on a simple feedforward multilayer perceptron (MLP) network with vanilla backward error propagation to achieve this aim. Six tests are reported that show the ability of an NN to distinguish six similar states of motion of a simplified human figure that has become obscured by moving vertical and horizontal bars and random blocks for different levels of obscuration. Four more extensive tests are then reported to determine the bounds of the technique. The results from the ENI network were compared to results from the same NN trained on clean states only. The results pilot strong evidence that it is possible to track a human subject behind objects using this technique, and thus this technique lends itself to a real-time markerless tracking system from a single video stream. [ABSTRACT FROM AUTHOR]
ISSN:	08997667
DOI:	10.1162/neco.2006.18.9.2122