scholarly journals Joint Classification and Regression for Visual Tracking with Fully Convolutional Siamese Networks

Author(s):  
Ying Cui ◽  
Dongyan Guo ◽  
Yanyan Shao ◽  
Zhenhua Wang ◽  
Chunhua Shen ◽  
...  

AbstractVisual tracking of generic objects is one of the fundamental but challenging problems in computer vision. Here, we propose a novel fully convolutional Siamese network to solve visual tracking by directly predicting the target bounding box in an end-to-end manner. We first reformulate the visual tracking task as two subproblems: a classification problem for pixel category prediction and a regression task for object status estimation at this pixel. With this decomposition, we design a simple yet effective Siamese architecture based classification and regression framework, termed SiamCAR, which consists of two subnetworks: a Siamese subnetwork for feature extraction and a classification-regression subnetwork for direct bounding box prediction. Since the proposed framework is both proposal- and anchor-free, SiamCAR can avoid the tedious hyper-parameter tuning of anchors, considerably simplifying the training. To demonstrate that a much simpler tracking framework can achieve superior tracking results, we conduct extensive experiments and comparisons with state-of-the-art trackers on a few challenging benchmarks. Without bells and whistles, SiamCAR achieves leading performance with a real-time speed. Furthermore, the ablation study validates that the proposed framework is effective with various backbone networks, and can benefit from deeper networks. Code is available at https://github.com/ohhhyeahhh/SiamCAR.

Author(s):  
Junseok Kwon ◽  
Junha Roh ◽  
Kyoung Mu Lee ◽  
Luc Van Gool

2021 ◽  
Author(s):  
Shan Liu ◽  
Lilian Huang ◽  
Xu Shi ◽  
Yi Sun

Author(s):  
Mustansar Fiaz ◽  
Md. Maklachur Rahman ◽  
Arif Mahmood ◽  
Sehar Shahzad Farooq ◽  
Ki Yeol Baek ◽  
...  

2020 ◽  
Author(s):  
John Hancock ◽  
Taghi M Khoshgoftaar

Abstract Gradient Boosted Decision Trees (GBDT's) are a powerful tool for classification and regression tasks in Big Data, Researchers should be familiar with the strengths and weaknesses of current implementations of GBDT's in order to use them effectively and make successful contributions. CatBoost is a member of the family of GBDT machine learning ensemble techniques. Since its debut in late 2018, researchers have ellCcessfully used CatBoost for machine learning studies involving Big Data. We take this opportunity to review recent research on CatBoost as it relates to Big Data, and learn best practices from studies that .55 CatBoost in a positive light, as well as studies where CatBoost does not outshine other techniques, since we can learn lessons from both types of scenarios. Furthermore, as a Decision Tree based algorithm, CatBoost is well-suited to machine learning tasks involving categorical, heterogeneous data. Recent work across multiple disciplines illustrates CatBoost's effectiveness and shortcomings in classification and regression tasks. Another important issue we expose in literature on CatBoost is its sensitivity to hyper-parameters and the importance of hyper-parameter tuning. One contribution we make is to take an interdisciplinary approach to cover studies related to CatBoost in a single work. This provides researchers an in-depth understanding to help clarify proper application of CatBoost in solving problems. To the best of our knowledge, this is the first survey that studies all works related to CatBoost in a single publication.


Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2145 ◽  
Author(s):  
Guoxu Liu ◽  
Joseph Christian Nouaze ◽  
Philippe Lyonel Touko Mbouembe ◽  
Jae Ho Kim

Automatic fruit detection is a very important benefit of harvesting robots. However, complicated environment conditions, such as illumination variation, branch, and leaf occlusion as well as tomato overlap, have made fruit detection very challenging. In this study, an improved tomato detection model called YOLO-Tomato is proposed for dealing with these problems, based on YOLOv3. A dense architecture is incorporated into YOLOv3 to facilitate the reuse of features and help to learn a more compact and accurate model. Moreover, the model replaces the traditional rectangular bounding box (R-Bbox) with a circular bounding box (C-Bbox) for tomato localization. The new bounding boxes can then match the tomatoes more precisely, and thus improve the Intersection-over-Union (IoU) calculation for the Non-Maximum Suppression (NMS). They also reduce prediction coordinates. An ablation study demonstrated the efficacy of these modifications. The YOLO-Tomato was compared to several state-of-the-art detection methods and it had the best detection performance.


2020 ◽  
Author(s):  
Thadeu Dias ◽  
Luiz Tavares ◽  
Rafael Padilla ◽  
Allan Silva ◽  
Lucas Thomaz ◽  
...  

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