Survey on Object Tracking using Deep Learning Paradigms

Combining Deep Learning and Preference Learning for Object Tracking

Visual Tracking Utilizing Object Concept from Deep Learning Network

Despite having achieved good performance, visual tracking is still an open area of research, especially when target undergoes serious appearance changes which are not included in the model. So, in this paper, we replace the appearance model by a concept model which is learned from large-scale datasets using a deep learning network. The concept model is a combination of high-level semantic infor...

Deep Reinforcement Learning for Visual Object Tracking in Videos

Convolutional neural network (CNN) models have achieved tremendous success in many visual detection and recognition tasks. Unfortunately, visual tracking, a fundamental computer vision problem, is not handled well using the existing CNN models, because most object trackers implemented with CNN do not effectively leverage temporal and contextual information among consecutive frames. Recurrent ne...

Deep Learning of Appearance Models for Online Object Tracking

This paper introduces a novel deep learning based approach for vision based single target tracking. We address this problem by proposing a network architecture which takes the input video frames and directly computes the tracking score for any candidate target location by estimating the probability distributions of the positive and negative examples. This is achieved by combining a deep convolu...

Formant Estimation and Tracking Using Deep Learning

Formant frequency estimation and tracking are among the most fundamental problems in speech processing. In the former task the input is a stationary speech segment such as the middle part of a vowel and the goal is to estimate the formant frequencies, whereas in the latter task the input is a series of speech frames and the goal is to track the trajectory of the formant frequencies throughout t...