Semi-supervised deep learning for object tracking and classification
Nikolaos D. Doulamis, Anastasios D. Doulamis · 2014
A semi-supervised deep learning paradigm is proposed for object classification/tracking. The method addresses the main difficulties of deep learning, by allowing unsupervised data to initially configure the network and then a gradient descent optimization scheme is triggered to fine tune the data. Unsupervised learning transforms the input data into smaller and more abstract forms of representations and therefore improves the stability, convergence and performance of the model. Additionally, an adaptive approach is presented in a way to allow dynamic modification of the model to the current visual conditions. Adaptation is performed by exploiting both unsupervised and supervised samples, coming by the application of a combined motion/deep learning tracker activating only at frames a decision mechanisms ascertains retraining.