scholarly journals Real-Time Object Tracking with Template Tracking and Foreground Detection Network

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3945
Author(s):  
Kaiheng Dai ◽  
Yuehuan Wang ◽  
Qiong Song
Keyword(s):  
Object Tracking ◽  
Real Time ◽  
Feature Representation ◽  
Foreground Detection ◽  
Tracking Accuracy ◽  
Convolutional Network ◽  
Backbone Network ◽  
Parallel Networks ◽  
Target Template ◽  
Template Tracking

In this paper, we propose a fast and accurate deep network-based object tracking method, which combines feature representation, template tracking and foreground detection into a single framework for robust tracking. The proposed framework consists of a backbone network, which feeds into two parallel networks, TmpNet for template tracking and FgNet for foreground detection. The backbone network is a pre-trained modified VGG network, in which a few parameters need to be fine-tuned for adapting to the tracked object. FgNet is a fully convolutional network to distinguish the foreground from background in a pixel-to-pixel manner. The parameter in TmpNet is the learned channel-wise target template, which initializes in the first frame and performs fast template tracking in the test frames. To enable each component to work closely with each other, we use a multi-task loss to end-to-end train the proposed framework. In online tracking, we combine the score maps from TmpNet and FgNet to find the optimal tracking results. Experimental results on object tracking benchmarks demonstrate that our approach achieves favorable tracking accuracy against the state-of-the-art trackers while running at a real-time speed of 38 fps.

scholarly journals Fast and Robust Object Tracking Using Tracking Failure Detection in Kernelized Correlation Filter

2020 ◽  
Vol 10 (2) ◽  
pp. 713 ◽  
Author(s):  
Jungsup Shin ◽  
Heegwang Kim ◽  
Dohun Kim ◽  
Joonki Paik
Keyword(s):  
Object Tracking ◽  
Real Time ◽  
Processing Speed ◽  
Motion Vector ◽  
Failure Detection ◽  
Correlation Filter ◽  
Tracking Accuracy ◽  
Practical Applications ◽  
Feature Based ◽  
Active Research

Object tracking has long been an active research topic in image processing and computer vision fields with various application areas. For practical applications, the object tracking technique should be not only accurate but also fast in a real-time streaming condition. Recently, deep feature-based trackers have been proposed to achieve a higher accuracy, but those are not suitable for real-time tracking because of an extremely slow processing speed. The slow speed is a major factor to degrade tracking accuracy under a real-time streaming condition since the processing delay forces skipping frames. To increase the tracking accuracy with preserving the processing speed, this paper presents an improved kernelized correlation filter (KCF)-based tracking method that integrates three functional modules: (i) tracking failure detection, (ii) re-tracking using multiple search windows, and (iii) motion vector analysis to decide a preferred search window. Under a real-time streaming condition, the proposed method yields better results than the original KCF in the sense of tracking accuracy, and when a target has a very large movement, the proposed method outperforms a deep learning-based tracker, such as multi-domain convolutional neural network (MDNet).

Research of Object Tracking Algorithm Based on BRISK

2014 ◽  
Vol 1049-1050 ◽  
pp. 1496-1501
Author(s):  
Shi Xu Guo ◽  
Jia Xin Chen ◽  
Bo Peng
Keyword(s):  
Object Tracking ◽  
Real Time ◽  
Feature Matching ◽  
Tracking Algorithm ◽  
Experimental Result ◽  
Moving Target ◽  
Target Template ◽  
Result Show ◽  
Search Feature ◽  
Set Up

In view of the problems that high complexity, large calculation and the difficulty to apply to real-time systems in the current moving target tracking algorithm, this paper introduce the BRISK feature extraction algorithm, and proposed the object tracking algorithm based on BRISK. Set up the background model and use the background difference method to detect the moving target template. Then match in the next frame and track the target. In order to reduce the search feature matching area, further improve the real-time of the algorithm, we also introduce the kalman filter algorithm to estimate the target motion trajectory. The experimental result show that comparing with the SURF, SIFT feature tracking algorithm, the algorithm of this paper has greatly improved in real-time.

scholarly journals Object Tracking in Hyperspectral-Oriented Video with Fast Spatial-Spectral Features

2021 ◽  
Vol 13 (10) ◽  
pp. 1922
Author(s):  
Lulu Chen ◽  
Yongqiang Zhao ◽  
Jiaxin Yao ◽  
Jiaxin Chen ◽  
Ning Li ◽  
...  
Keyword(s):  
Object Tracking ◽  
Real Time ◽  
Spatial Information ◽  
Video Tracking ◽  
Spectral Features ◽  
Surveillance Video ◽  
Tracking Accuracy ◽  
Low Resolution ◽  
Background Clutter ◽  
Appearance Changes

This paper presents a correlation filter object tracker based on fast spatial-spectral features (FSSF) to realize robust, real-time object tracking in hyperspectral surveillance video. Traditional object tracking in surveillance video based only on appearance information often fails in the presence of background clutter, low resolution, and appearance changes. Hyperspectral imaging uses unique spectral properties as well as spatial information to improve tracking accuracy in such challenging environments. However, the high-dimensionality of hyperspectral images causes high computational costs and difficulties for discriminative feature extraction. In FSSF, the real-time spatial-spectral convolution (RSSC) kernel is updated in real time in the Fourier transform domain without offline training to quickly extract discriminative spatial-spectral features. The spatial-spectral features are integrated into correlation filters to complete the hyperspectral tracking. To validate the proposed scheme, we collected a hyperspectral surveillance video (HSSV) dataset consisting of 70 sequences in 25 bands. Extensive experiments confirm the advantages and the efficiency of the proposed FSSF for object tracking in hyperspectral video tracking in challenging conditions of background clutter, low resolution, and appearance changes.

scholarly journals Multiple Ship Tracking in Remote Sensing Images Using Deep Learning

2021 ◽  
Vol 13 (18) ◽  
pp. 3601
Author(s):  
Jin Wu ◽  
Changqing Cao ◽  
Yuedong Zhou ◽  
Xiaodong Zeng ◽  
Zhejun Feng ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Object Tracking ◽  
Real Time ◽  
Tracking Accuracy ◽  
Remote Sensing Images ◽  
Tracking Algorithms ◽  
Similar Images ◽  
Real Time Tracking ◽  
Multiple Granularity

In remote sensing images, small target size and diverse background cause difficulty in locating targets accurately and quickly. To address the lack of accuracy and inefficient real-time performance of existing tracking algorithms, a multi-object tracking (MOT) algorithm for ships using deep learning was proposed in this study. The feature extraction capability of target detectors determines the performance of MOT algorithms. Therefore, you only look once (YOLO)-v3 model, which has better accuracy and speed than other algorithms, was selected as the target detection framework. The high similarity of ship targets will cause poor tracking results; therefore, we used the multiple granularity network (MGN) to extract richer target appearance information to improve the generalization ability of similar images. We compared the proposed algorithm with other state-of-the-art multi-object tracking algorithms. Results show that the tracking accuracy is improved by 2.23%, while the average running speed is close to 21 frames per second, meeting the needs of real-time tracking.

scholarly journals Target tracking accuracy and latency with different 4D ultrasound systems – a robotic phantom study

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Svenja Ipsen ◽  
Sven Böttger ◽  
Holger Schwegmann ◽  
Floris Ernst
Keyword(s):  
Target Tracking ◽  
Real Time ◽  
Template Matching ◽  
Image Data ◽  
Distinct Advantage ◽  
Reference Trajectory ◽  
Data Streaming ◽  
Tracking Accuracy ◽  
Time Data ◽  
4D Ultrasound

AbstractUltrasound (US) imaging, in contrast to other image guidance techniques, offers the distinct advantage of providing volumetric image data in real-time (4D) without using ionizing radiation. The goal of this study was to perform the first quantitative comparison of three different 4D US systems with fast matrix array probes and real-time data streaming regarding their target tracking accuracy and system latency. Sinusoidal motion of varying amplitudes and frequencies was used to simulate breathing motion with a robotic arm and a static US phantom. US volumes and robot positions were acquired online and stored for retrospective analysis. A template matching approach was used for target localization in the US data. Target motion measured in US was compared to the reference trajectory performed by the robot to determine localization accuracy and system latency. Using the robotic setup, all investigated 4D US systems could detect a moving target with sub-millimeter accuracy. However, especially high system latency increased tracking errors substantially and should be compensated with prediction algorithms for respiratory motion compensation.

scholarly journals Attention Modulated Multiple Object Tracking with Motion Enhancement and Dual Correlation

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 266 ◽  
Author(s):  
Yifeng Wang ◽  
Zhijiang Zhang ◽  
Ning Zhang ◽  
Dan Zeng
Keyword(s):  
Object Tracking ◽  
Multiple Object Tracking ◽  
Tracking Accuracy ◽  
Feature Maps ◽  
Backbone Networks ◽  
Multiple Object ◽  
The Arts ◽  
Training Stage ◽  
The One ◽  
Baseline State

The one-shot multiple object tracking (MOT) framework has drawn more and more attention in the MOT research community due to its advantage in inference speed. However, the tracking accuracy of current one-shot approaches could lead to an inferior performance compared with their two-stage counterparts. The reasons are two-fold: one is that motion information is often neglected due to the single-image input. The other is that detection and re-identification (ReID) are two different tasks with different focuses. Joining detection and re-identification at the training stage could lead to a suboptimal performance. To alleviate the above limitations, we propose a one-shot network named Motion and Correlation-Multiple Object Tracking (MAC-MOT). MAC-MOT introduces a motion enhance attention module (MEA) and a dual correlation attention module (DCA). MEA performs differences on adjacent feature maps which enhances the motion-related features while suppressing irrelevant information. The DCA module focuses on decoupling the detection task and re-identification task to strike a balance and reduce the competition between these two tasks. Moreover, symmetry is a core design idea in our proposed framework which is reflected in Siamese-based deep learning backbone networks, the input of dual stream images, as well as a dual correlation attention module. Our proposed approach is evaluated on the popular multiple object tracking benchmarks MOT16 and MOT17. We demonstrate that the proposed MAC-MOT can achieve a better performance than the baseline state of the arts (SOTAs).

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