Unsupervised Tracking of Stereoscopic Video Objects Employing Neural Networks Retraining

A novel approach is presented in this paper for improving the performance of neural network classifiers in video object tracking applications, based on a retraining procedure at the user level. The procedure includes (a) a retraining algorithm for adapting the network weights to the current conditions, (b) semantically meaningful object extraction which plays the role of the retraining set and (c) a decision mechanism for determining when network retraining should be activated. The retraining algorithm takes into consideration both the former and the current network knowledge in order to achieve good generalization and reduce retraining time. Object extraction is accomplished by utilizing depth information, provided by stereoscopic video and incorporating a multiresolution implementation of the Recursive Shortest Spanning Tree (RSST) segmentation algorithm. Finally the decision mechanism in this framework depends on a scene change detection algorithm. Results are presented which illustrate the performance of the proposed approach in real life experiments.