Siamese Learning Visual Tracking: A Survey

07/03/2017
by   Roman Pflugfelder, et al.
0

The aim of this survey is an attempt to review the kind of machine learning and stochastic techniques and the ways existing work currently uses machine learning and stochastic methods for the challenging problem of visual tracking. It is not intended to study the whole tracking literature of the last decades as this seems impossible by the incredible vast number of published papers. This first draft version of the article focuses very targeted on recent literature that suggests Siamese networks for the learning of tracking. This approach promise a step forward in terms of robustness, accuracy and computational efficiency. For example, the representative tracker SINT performs currently best on the popular OTB-2013 benchmark with AuC/IoU/prec. 65.5/62.5/84.8 the Oxford group shows the approach's large potential of HW/SW co-design with network memory needs around 600 kB and frame-rates of 75 fps and beyond. Before a detailed description of this approach is given, the article recaps the definition of tracking, the current state-of-the-art view on designing algorithms and the state-of-the-art of trackers by summarising insights from existing literature. In future, the article will be extended by the review of two alternative approaches, the one using very general recurrent networks such as the Long Shortterm Memory (LSTM) networks and the other most obvious approach of applying sole convolutional networks (CNN), the earliest approach since the idea of deep learning tracking appeared at NIPS'13.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
01/23/2022

A Survey for Deep RGBT Tracking

Visual object tracking with the visible (RGB) and thermal infrared (TIR)...
research
11/24/2022

Multi-Template Temporal Siamese Network for Long-Term Object Tracking

Siamese Networks are one of most popular visual object tracking methods ...
research
12/06/2021

Visual Object Tracking with Discriminative Filters and Siamese Networks: A Survey and Outlook

Accurate and robust visual object tracking is one of the most challengin...
research
12/31/2018

SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networks

Siamese network based trackers formulate tracking as convolutional featu...
research
10/13/2015

Hybrid Dialog State Tracker

This paper presents a hybrid dialog state tracker that combines a rule b...
research
03/19/2021

DCF-ASN: Coarse-to-fine Real-time Visual Tracking via Discriminative Correlation Filter and Attentional Siamese Network

Discriminative correlation filters (DCF) and siamese networks have achie...
research
10/13/2009

State of the Art Review for Applying Computational Intelligence and Machine Learning Techniques to Portfolio Optimisation

Computational techniques have shown much promise in the field of Finance...

Please sign up or login with your details

Forgot password? Click here to reset