Journal ArticleDOI
Visual object tracking: A survey
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TLDR
A comprehensive overview of state-of-the-art tracking frameworks including both deep and non-deep trackers is provided in this article , where the authors present both quantitative and qualitative tracking results of various trackers on five benchmark datasets.About:
This article is published in Computer Vision and Image Understanding.The article was published on 2022-07-01. It has received 39 citations till now. The article focuses on the topics: BitTorrent tracker & Computer science.read more
Citations
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Journal ArticleDOI
Visual object tracking with adaptive structural convolutional network
TL;DR: This paper proposes an adaptive structural convolutional filter model to enhance the robustness of deep regression trackers (named: ASCT), and develops an end-to-end trainable network comprising feature extraction, decision making, and model updating modules for effective training.
Journal ArticleDOI
Advances in Deep Learning Methods for Visual Tracking: Literature Review and Fundamentals
TL;DR: This paper provides a comprehensive review of state-of-the-art single-object tracking algorithms based on deep learning, and introduces basic knowledge of deep visual tracking, including fundamental concepts, existing algorithms, and previous reviews.
Journal ArticleDOI
Inverted Residual Siamese Visual Tracking With Feature Crossing Network
TL;DR: Wang et al. as mentioned in this paper proposed an inverted residual Siamese feature-crossing network (IRSiamese-FCN) which is end-to-end trained off-line with a large amount of image pairs.
Journal ArticleDOI
Real-time multiple object tracking using deep learning methods
TL;DR: A real-time multiple-object tracking framework that is based on a modified version of the Deep SORT algorithm based on the process of the initialization of the objects, and its rationale is to consider an object as tracked if it is detected in a set of previous frames.
Journal ArticleDOI
Continuously Adaptive Mean-shift Tracking Algorithm Based on Improved Gaussian Model
TL;DR: In this article, a target tracking algorithm based on an improved Gaussian mixture model was proposed to improve the accuracy of the traditional continuously adaptive mean-shift algorithm (CAMShift) in complex scenarios.
References
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Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Journal ArticleDOI
Long short-term memory
TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
Proceedings Article
Attention is All you Need
Ashish Vaswani,Noam Shazeer,Niki Parmar,Jakob Uszkoreit,Llion Jones,Aidan N. Gomez,Lukasz Kaiser,Illia Polosukhin +7 more
TL;DR: This paper proposed a simple network architecture based solely on an attention mechanism, dispensing with recurrence and convolutions entirely and achieved state-of-the-art performance on English-to-French translation.
Journal ArticleDOI
Distinctive Image Features from Scale-Invariant Keypoints
TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.
Journal ArticleDOI
ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky,Jia Deng,Hao Su,Jonathan Krause,Sanjeev Satheesh,Sean Ma,Zhiheng Huang,Andrej Karpathy,Aditya Khosla,Michael S. Bernstein,Alexander C. Berg,Li Fei-Fei +11 more
TL;DR: The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) as mentioned in this paper is a benchmark in object category classification and detection on hundreds of object categories and millions of images, which has been run annually from 2010 to present, attracting participation from more than fifty institutions.