Knowledge Extraction in Stereo Video Sequences Using Adaptive Neural Networks

Abstract

In this chapter, an adaptive neural network architecture is proposed for efficient knowledge extraction in video sequences. The system is focused on video object segmentation and tracking in stereoscopic video sequences. The proposed scheme includes: (a) a retraining algorithm for adapting the network weights to current conditions, (b) a semantically meaningful object extraction module for creating a retraining set and (c) a decision mechanism, which detects the time instances when a new network retraining is activated. The retraining algorithm optimally adapts network weights by exploiting information of the current conditions with a minimal deviation of the network weights. The algorithm results in the minimization of a convex function subject to linear constraints, and thus, one minimum exists. Description of current conditions is provided by a segmentation fusion scheme, which appropriately combines color and depth information. Experimental results on real-life video sequences are presented to indicate the promising performance of the proposed adaptive neural network-based scheme.