scholarly journals TrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the Wild

2018 ◽  
pp. 310-327 ◽  
Author(s):  
Matthias Müller ◽  
Adel Bibi ◽  
Silvio Giancola ◽  
Salman Alsubaihi ◽  
Bernard Ghanem
Keyword(s):  
Object Tracking ◽  
Large Scale ◽  
Large Scale Dataset ◽  
In The Wild

scholarly journals Proposal-Based Visual Tracking Using Spatial Cascaded Transformed Region Proposal Network

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4810
Author(s):  
Ximing Zhang ◽  
Shujuan Luo ◽  
Xuewu Fan
Keyword(s):  
Object Tracking ◽  
Spatial Representation ◽  
Large Scale ◽  
Similarity Score ◽  
Visual Object ◽  
Training Procedure ◽  
Single Object ◽  
Visual Object Tracking ◽  
Large Scale Dataset ◽  
In The Wild

Region proposal network (RPN) based trackers employ the classification and regression block to generate the proposals, the proposal that contains the highest similarity score is formulated to be the groundtruth candidate of next frame. However, region proposal network based trackers cannot make the best of the features from different convolutional layers, and the original loss function cannot alleviate the data imbalance issue of the training procedure. We propose the Spatial Cascaded Transformed RPN to combine the RPN and STN (spatial transformer network) together, in order to successfully obtain the proposals of high quality, which can simultaneously improves the robustness. The STN can transfer the spatial transformed features though different stages, which extends the spatial representation capability of such networks handling complex scenarios such as scale variation and affine transformation. We break the restriction though an easy samples penalization loss (shrinkage loss) instead of smooth L1 function. Moreover, we perform the multi-cue proposals re-ranking to guarantee the accuracy of the proposed tracker. We extensively prove the effectiveness of our proposed method on the ablation studies of the tracking datasets, which include OTB-2015 (Object Tracking Benchmark 2015), VOT-2018 (Visual Object Tracking 2018), LaSOT (Large Scale Single Object Tracking), TrackingNet (A Large-Scale Dataset and Benchmark for Object Tracking in the Wild) and UAV123 (UAV Tracking Dataset).

scholarly journals Search Tracker: Human-Derived Object Tracking in the Wild Through Large-Scale Search and Retrieval

2017 ◽  
Vol 27 (8) ◽  
pp. 1803-1814 ◽  
Author(s):  
Archith John Bency ◽  
S. Karthikeyan ◽  
Carter De Leo ◽  
Santhoshkumar Sunderrajan ◽  
B. S. Manjunath
Keyword(s):  
Object Tracking ◽  
Large Scale ◽  
In The Wild ◽  
Search And Retrieval

scholarly journals CNN-Based Target Recognition and Identification for Infrared Imaging in Defense Systems

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2040 ◽  
Author(s):  
Antoine d’Acremont ◽  
Ronan Fablet ◽  
Alexandre Baussard ◽  
Guillaume Quin
Keyword(s):  
Large Scale ◽  
Data Augmentation ◽  
Infrared Imaging ◽  
State Of The Art ◽  
Object Identification ◽  
Fine Tuning ◽  
Support Vector ◽  
Defense Systems ◽  
Large Scale Dataset ◽  
In The Wild

Convolutional neural networks (CNNs) have rapidly become the state-of-the-art models for image classification applications. They usually require large groundtruthed datasets for training. Here, we address object identification and recognition in the wild for infrared (IR) imaging in defense applications, where no such large-scale dataset is available. With a focus on robustness issues, especially viewpoint invariance, we introduce a compact and fully convolutional CNN architecture with global average pooling. We show that this model trained from realistic simulation datasets reaches a state-of-the-art performance compared with other CNNs with no data augmentation and fine-tuning steps. We also demonstrate a significant improvement in the robustness to viewpoint changes with respect to an operational support vector machine (SVM)-based scheme.

scholarly journals OASIS: A Large-Scale Dataset for Single Image 3D in the Wild

2020 ◽  
Author(s):  
Weifeng Chen ◽  
Shengyi Qian ◽  
David Fan ◽  
Noriyuki Kojima ◽  
Max Hamilton ◽  
...  
Keyword(s):  
Large Scale ◽  
Single Image ◽  
Large Scale Dataset ◽  
In The Wild

VSPW: A Large-scale Dataset for Video Scene Parsing in the Wild

2021 ◽  
Author(s):  
Jiaxu Miao ◽  
Yunchao Wei ◽  
Yu Wu ◽  
Chen Liang ◽  
Guangrui Li ◽  
...  
Keyword(s):  
Large Scale ◽  
Scene Parsing ◽  
Large Scale Dataset ◽  
Video Scene ◽  
In The Wild

scholarly journals Objectron: A Large Scale Dataset of Object-Centric Videos in the Wild with Pose Annotations

2021 ◽  
Author(s):  
Adel Ahmadyan ◽  
Liangkai Zhang ◽  
Artsiom Ablavatski ◽  
Jianing Wei ◽  
Matthias Grundmann
Keyword(s):  
Large Scale ◽  
Large Scale Dataset ◽  
In The Wild

Planar Object Tracking Benchmark in the Wild

2021 ◽  
Author(s):  
Pengpeng Liang ◽  
Haoxuanye Ji ◽  
Yifan Wu ◽  
Yumei Chai ◽  
Liming Wang ◽  
...  
Keyword(s):  
Object Tracking ◽  
In The Wild

scholarly journals Joint regression and learning from pairwise rankings for personalized image aesthetic assessment

2021 ◽  
Author(s):  
Jin Zhou ◽  
Qing Zhang ◽  
Jian-Hao Fan ◽  
Wei Sun ◽  
Wei-Shi Zheng
Keyword(s):  
Large Scale ◽  
Assessment Model ◽  
Generic Model ◽  
Small Subset ◽  
Deep Convolutional Neural Networks ◽  
Personal Taste ◽  
Hinge Loss ◽  
Novel Approach ◽  
Large Scale Dataset ◽  
Image Pairs

AbstractRecent image aesthetic assessment methods have achieved remarkable progress due to the emergence of deep convolutional neural networks (CNNs). However, these methods focus primarily on predicting generally perceived preference of an image, making them usually have limited practicability, since each user may have completely different preferences for the same image. To address this problem, this paper presents a novel approach for predicting personalized image aesthetics that fit an individual user’s personal taste. We achieve this in a coarse to fine manner, by joint regression and learning from pairwise rankings. Specifically, we first collect a small subset of personal images from a user and invite him/her to rank the preference of some randomly sampled image pairs. We then search for the K-nearest neighbors of the personal images within a large-scale dataset labeled with average human aesthetic scores, and use these images as well as the associated scores to train a generic aesthetic assessment model by CNN-based regression. Next, we fine-tune the generic model to accommodate the personal preference by training over the rankings with a pairwise hinge loss. Experiments demonstrate that our method can effectively learn personalized image aesthetic preferences, clearly outperforming state-of-the-art methods. Moreover, we show that the learned personalized image aesthetic benefits a wide variety of applications.

scholarly journals VIPPrint: Validating Synthetic Image Detection and Source Linking Methods on a Large Scale Dataset of Printed Documents

2021 ◽  
Vol 7 (3) ◽  
pp. 50
Author(s):  
Anselmo Ferreira ◽  
Ehsan Nowroozi ◽  
Mauro Barni
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Child Pornography ◽  
Forensic Analysis ◽  
Synthetic Image ◽  
Image Detection ◽  
Face Images ◽  
Large Scale Dataset ◽  
Scanned Images ◽  
Analysis Of The Images

The possibility of carrying out a meaningful forensic analysis on printed and scanned images plays a major role in many applications. First of all, printed documents are often associated with criminal activities, such as terrorist plans, child pornography, and even fake packages. Additionally, printing and scanning can be used to hide the traces of image manipulation or the synthetic nature of images, since the artifacts commonly found in manipulated and synthetic images are gone after the images are printed and scanned. A problem hindering research in this area is the lack of large scale reference datasets to be used for algorithm development and benchmarking. Motivated by this issue, we present a new dataset composed of a large number of synthetic and natural printed face images. To highlight the difficulties associated with the analysis of the images of the dataset, we carried out an extensive set of experiments comparing several printer attribution methods. We also verified that state-of-the-art methods to distinguish natural and synthetic face images fail when applied to print and scanned images. We envision that the availability of the new dataset and the preliminary experiments we carried out will motivate and facilitate further research in this area.

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