GRSiamFC: Group Residual Convolutional Siamese Networks for Object Tracking

2021 ◽  
Author(s):  
Hanlin Huang ◽  
Guixi Liu ◽  
Yubo Liu ◽  
Yi Zhang
Keyword(s):  
Object Tracking ◽  
Siamese Networks

scholarly journals Distractor-Aware Siamese Networks for Visual Object Tracking

2018 ◽  
pp. 103-119 ◽  
Author(s):  
Zheng Zhu ◽  
Qiang Wang ◽  
Bo Li ◽  
Wei Wu ◽  
Junjie Yan ◽  
...  
Keyword(s):  
Object Tracking ◽  
Visual Object ◽  
Visual Object Tracking ◽  
Siamese Networks

Robust real-time visual object tracking via multi-scale fully convolutional Siamese networks

2018 ◽  
Vol 77 (17) ◽  
pp. 22131-22143 ◽  
Author(s):  
Longchao Yang ◽  
Peilin Jiang ◽  
Fei Wang ◽  
Xuan Wang
Keyword(s):  
Object Tracking ◽  
Real Time ◽  
Visual Object ◽  
Visual Object Tracking ◽  
Multi Scale ◽  
Siamese Networks

scholarly journals Multi-Object Tracking Algorithm for RGB-D Images Based on Asymmetric Dual Siamese Networks

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6745
Author(s):  
Wen-Li Zhang ◽  
Kun Yang ◽  
Yi-Tao Xin ◽  
Ting-Song Zhao
Keyword(s):  
Object Tracking ◽  
Human Error ◽  
Tracking Algorithm ◽  
Movement Trajectory ◽  
Shopping Malls ◽  
Security Systems ◽  
Depth Images ◽  
Combined Features ◽  
Intelligent Security ◽  
Siamese Networks

Currently, intelligent security systems are widely deployed in indoor buildings to ensure the safety of people in shopping malls, banks, train stations, and other indoor buildings. Multi-Object Tracking (MOT), as an important component of intelligent security systems, has received much attention from many researchers in recent years. However, existing multi-objective tracking algorithms still suffer from trajectory drift and interruption problems in crowded scenes, which cannot provide valuable data for managers. In order to solve the above problems, this paper proposes a Multi-Object Tracking algorithm for RGB-D images based on Asymmetric Dual Siamese networks (ADSiamMOT-RGBD). This algorithm combines appearance information from RGB images and target contour information from depth images. Furthermore, the attention module is applied to repress the redundant information in the combined features to overcome the trajectory drift problem. We also propose a trajectory analysis module, which analyzes whether the head movement trajectory is correct in combination with time-context information. It reduces the number of human error trajectories. The experimental results show that the proposed method in this paper has better tracking quality on the MICC, EPFL, and UMdatasets than the previous work.

scholarly journals Object tracking framework with Siamese network and re-detection mechanism

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Daqun Li ◽  
Yi Yu ◽  
Xiaolin Chen
Keyword(s):  
Object Tracking ◽  
Objective Evaluation ◽  
Tracking Accuracy ◽  
Detection Mechanism ◽  
Siamese Network ◽  
Tracking Ability ◽  
Background Clutter ◽  
Instance Search ◽  
Template Updating ◽  
Siamese Networks

AbstractTo improve the deficient tracking ability of fully-convolutional Siamese networks (SiamFC) in complex scenes, an object tracking framework with Siamese network and re-detection mechanism (Siam-RM) is proposed. The mechanism adopts the Siamese instance search tracker (SINT) as the re-detection network. When multiple peaks appear on the response map of SiamFC, a more accurate re-detection network can re-determine the location of the object. Meanwhile, for the sake of adapting to various changes in appearance of the object, this paper employs a generative model to construct the templates of SiamFC. Furthermore, a method of template updating with high confidence is also used to prevent the template from being contaminated. Objective evaluation on the popular online tracking benchmark (OTB) shows that the tracking accuracy and the success rate of the proposed framework can reach 79.8% and 63.8%, respectively. Compared to SiamFC, the results of several representative video sequences demonstrate that our framework has higher accuracy and robustness in scenes with fast motion, occlusion, background clutter, and illumination variations.

scholarly journals Siamese anchor-free object tracking with multiscale spatial attentions

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jianming Zhang ◽  
Benben Huang ◽  
Zi Ye ◽  
Li-Dan Kuang ◽  
Xin Ning
Keyword(s):  
Object Tracking ◽  
Spatial Information ◽  
Target Position ◽  
Visual Object ◽  
Feature Maps ◽  
Free Object ◽  
Widespread Application ◽  
Great Performance ◽  
Free Classification ◽  
Siamese Networks

AbstractRecently, object trackers based on Siamese networks have attracted considerable attentions due to their remarkable tracking performance and widespread application. Especially, the anchor-based methods exploit the region proposal subnetwork to get accurate prediction of a target and make great performance improvement. However, those trackers cannot capture the spatial information very well and the pre-defined anchors will hinder robustness. To solve these problems, we propose a Siamese-based anchor-free object tracking algorithm with multiscale spatial attentions in this paper. Firstly, we take ResNet-50 as the backbone network to generate multiscale features of both template patch and search regions. Secondly, we propose the spatial attention extraction (SAE) block to capture the spatial information among all positions in the template and search region feature maps. Thirdly, we put these features into the SAE block to get the multiscale spatial attentions. Finally, an anchor-free classification and regression subnetwork is used for predicting the location of the target. Unlike anchor-based methods, our tracker directly predicts the target position without predefined parameters. Extensive experiments with state-of-the-art trackers are carried out on four challenging visual object tracking benchmarks: OTB100, UAV123, VOT2016 and GOT-10k. Those experimental results confirm the effectiveness of our proposed tracker.

scholarly journals A Learning Frequency-Aware Feature Siamese Network for Real-Time Visual Tracking

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 854
Author(s):  
Yuxiang Yang ◽  
Weiwei Xing ◽  
Shunli Zhang ◽  
Qi Yu ◽  
Xiaoyu Guo ◽  
...  
Keyword(s):  
Object Tracking ◽  
Low Frequency ◽  
Linear Representation ◽  
Visual Object ◽  
Tracking Accuracy ◽  
Discriminative Ability ◽  
Visual Object Tracking ◽  
Training Samples ◽  
Complex Scenes ◽  
Siamese Networks

Visual object tracking by Siamese networks has achieved favorable performance in accuracy and speed. However, the features used in Siamese networks have spatially redundant information, which increases computation and limits the discriminative ability of Siamese networks. Addressing this issue, we present a novel frequency-aware feature (FAF) method for robust visual object tracking in complex scenes. Unlike previous works, which select features from different channels or layers, the proposed method factorizes the feature map into multi-frequency and reduces the low-frequency information that is spatially redundant. By reducing the low-frequency map’s resolution, the computation is saved and the receptive field of the layer is also increased to obtain more discriminative information. To further improve the performance of the FAF, we design an innovative data-independent augmentation for object tracking to improve the discriminative ability of tracker, which enhanced linear representation among training samples by convex combinations of the images and tags. Finally, a joint judgment strategy is proposed to adjust the bounding box result that combines intersection-over-union (IoU) and classification scores to improve tracking accuracy. Extensive experiments on 5 challenging benchmarks demonstrate that our FAF method performs favorably against SOTA tracking methods while running around 45 frames per second.

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