Researches advanced in object tracking based on deep learning

Ziyuan Lu; Zhenkun Wang

doi:10.1117/12.2641826

10 November 2022 Researches advanced in object tracking based on deep learning

Ziyuan Lu, Zhenkun Wang

Proceedings Volume 12348, 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022); 123481L (2022) https://doi.org/10.1117/12.2641826
Event: 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022), 2022, Zhuhai, China

Abstract

Object tracking has always been one of the hot tasks in the computer vision community. Given the object’s size and position in the initial frame of a certain video sequence, the task of object tracking aims to predict the object’s size and position in each subsequent frame. Thanks to the rapid development of convolutional neural networks, the object tracking has achieved breakthroughs in accuracy and speed, while still faces many challenges in the large-scale applications such as appearance distortion, lighting changes, motion blur and similar background interference. In this paper, with in-depth literatures survey, we first introduce the typical tracking methods based on machine learning. We also detail the representative tracking methods based deep learning from the perspective of convolutional neural networks, recurrent neural networks, generative adversarial networks, autoencoders and other models. In addition, we analyze the performance of these representative algorithms on the common tracking datasets. Finally, we point out the unsolved problems and prospect the possible future development direction in research field of object tracking.

Citation Download Citation

Ziyuan Lu and Zhenkun Wang "Researches advanced in object tracking based on deep learning", Proc. SPIE 12348, 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022), 123481L (10 November 2022); https://doi.org/10.1117/12.2641826

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KEYWORDS

Detection and tracking algorithms

Optical tracking

Machine learning

Video

Neural networks

Data modeling

Convolutional neural networks

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