Ensemble Modeling with Convolutional Neural Networks for Application in Visual Object Tracking

Minji Kim, Ilchae Jung, Bohyung Han

http://doi.org/10.5626/JOK.2021.48.2.211

In the area of computer vision, visual object tracking aims to estimate the status of a target object from an input video stream, which can be broadly applicable to industries such as surveillance and the military. Recently, deep learning-based tracking algorithms have gone through significant improvements by using tracking-by-detection or template-based approach. However, these approaches are still suffering from inherent limitations caused by each strategy. In this paper, we propose a novel method to model ensemble trackers by fusing the two strategies, tracking-by-detection and template-based approach. We report significantly enhanced performance on widely adopted visual object tracking benchmarks, OTB100, UAV123, and LaSOT.