Single-target localization in video sequences using offline deep-ranked metric learning and online learned models updating

Abstract

Foreground targets localization in video sequences receives much popularity in computer vision during the past few years, and its studies are highly related toward machine learning techniques. Driven by the recent popular deep learning techniques in machine learning, many contemporary localization studies are equipped with popular deep learning methods, and their performance has been benefited a lot by the prominent generalization capability of deep learning methods. In this study, inspired by deep metric learning, which is a new trend in deep learning, a novel single-target localization method is proposed. This new method is composed of two steps. First, an offline deep-ranked metric learning step is fulfilled and its gradient at the end-to-end learning procedure of the whole deep learning model is derived for realizing the conventional stochastic gradient algorithm. Also, an alternative proximal gradient algorithm is introduced to boost the efficiency as well. Second, an online models updating step is employed by the consecutive updating manner as well as the incremental updating manner, in order to make the offline learned outcome more adaptive during the progression of video sequences, in which challenging circumstances, such as sudden illumination changes, obstacles, shape transformation, complex background, etc., are likely to occur. This new single-target localization method has been compared with several shallow learning-based or deep learning-based localization methods in a large video database. Both qualitative and quantitative analysis have been comprehensively conducted to reveal the superiority of the new single-target localization method from the statistical point of view.