scholarly journals Improvement of the KCF Tracking Algorithm through Object Detection

2018 ◽  
Vol 7 (4.4) ◽  
pp. 11
Author(s):  
Jae Wan Park ◽  
Sungjoong Kim ◽  
Youngjae Lee ◽  
Inwhee Joe
Keyword(s):  
Object Detection ◽  
Object Tracking ◽  
Tracking Algorithm ◽  
Correlation Filter

When the position of the beam projector is changed, users have to manually adjust the position. In this paper, we propose a system that can automatically correct images. In this process, the KCF (Kernelized Correlation Filter) algorithm is used for tracking the IR (Infrared) markers. We analyze the object tracking failure problem of the KCF and improve the KCF tracking algorithm that solves the problem through object detection.  

Object tracking algorithm based on two-step correlation filter

2018 ◽  
Vol 47 (12) ◽  
pp. 1226004
Author(s):  
葛宝义 Ge Baoyi ◽  
左宪章 Zuo Xianzhang ◽  
胡永江 Hu Yongjiang ◽  
张 岩 Zhang Yan
Keyword(s):  
Object Tracking ◽  
Tracking Algorithm ◽  
Correlation Filter

Long-Term Object Tracking Algorithm Based on Kernelized Correlation Filter

2019 ◽  
Vol 56 (1) ◽  
pp. 010702
Author(s):  
茅正冲 Mao Zhengchong ◽  
陈海东 Chen Haidong
Keyword(s):  
Object Tracking ◽  
Tracking Algorithm ◽  
Correlation Filter

Object Tracking Algorithm based on Improved Siamese Convolutional Networks Combined with Deep Contour Extraction and Object Detection Under Airborne Platform

2020 ◽  
Vol 64 (4) ◽  
pp. 40409-1-40409-11
Author(s):  
Xiuyan Tian ◽  
Haifang Li ◽  
Hongxia Deng
Keyword(s):  
Object Detection ◽  
Object Tracking ◽  
Clustering Algorithm ◽  
Object Perception ◽  
Tracking Algorithm ◽  
Tracking Performance ◽  
Contour Extraction ◽  
Data Set ◽  
Siamese Network ◽  
Detection And Tracking

Abstract Object detection and tracking is an indispensable module in airborne optoelectronic equipment, and its detection and tracking performance is directly related to the accuracy of object perception. Recently, the improved Siamese network tracking algorithm has achieved excellent results on various challenging data sets. However, most of the improved algorithms use local fixed search strategies, which cannot update the template. In addition, the template will introduce background interference, which will lead to tracking drift and eventually cause tracking failure. In order to solve these problems, this article proposes an improved fully connected Siamese tracking algorithm combined with object contour extraction and object detection, which uses the contour template of the object instead of the bounding-box template to reduce the background clutter interference. First, the contour detection network automatically obtains the closed contour information of the object and uses the flood-filling clustering algorithm to obtain the contour template. Then, the contour template and the search area are fed into the improved Siamese network to obtain the optimal tracking score value and adaptively update the contour template. If the object is fully obscured or lost, the YoLo v3 network is used to search the object in the entire field of view to achieve stable tracking throughout the process. A large number of qualitative and quantitative simulation results on benchmark test data set and the flying data set show that the improved model can not only improve the object tracking performance under complex backgrounds, but also improve the response time of airborne systems, which has high engineering application value.

scholarly journals Robust long-term object tracking with adaptive scale and rotation estimation

2020 ◽  
Vol 17 (2) ◽  
pp. 172988142090973
Author(s):  
Huimin Lu ◽  
Dan Xiong ◽  
Junhao Xiao ◽  
Zhiqiang Zheng
Keyword(s):  
Object Tracking ◽  
Estimation Method ◽  
Tracking Algorithm ◽  
Correlation Filter ◽  
Estimation Accuracy ◽  
Continuous Space ◽  
Searching Method ◽  
Object Searching ◽  
Rotation Estimation

In this article, a robust long-term object tracking algorithm is proposed. It can tackle the challenges of scale and rotation changes during the long-term object tracking for security robots. Firstly, a robust scale and rotation estimation method is proposed to deal with scale changes and rotation motion of the object. It is based on the Fourier–Mellin transform and the kernelized correlation filter. The object’s scale and rotation can be estimated in the continuous space, and the kernelized correlation filter is used to improve the estimation accuracy and robustness. Then a weighted object searching method based on the histogram and the variance is introduced to handle the problem that trackers may fail in the long-term object tracking (due to semi-occlusion or full occlusion). When the tracked object is lost, the object can be relocated in the whole image using the searching method, so the tracker can be recovered from failures. Moreover, two other kernelized correlation filters are learned to estimate the object’s translation and the confidence of tracking results, respectively. The estimated confidence is more accurate and robust using the dedicatedly designed kernelized correlation filter, which is employed to activate the weighted object searching module, and helps to determine whether the searching windows contain objects. We compare the proposed algorithm with state-of-the-art tracking algorithms on the online object tracking benchmark. The experimental results validate the effectiveness and superiority of our tracking algorithm.

scholarly journals THE OBJECT TRACKING ALGORITHM USING DIMENSIONAL BASED DETECTION FOR PUBLIC STREET ENVIRONMENT

2020 ◽  
Vol 17 (2) ◽  
pp. 123-127
Author(s):  
I. G. Matveev ◽  
Keyword(s):  
Object Detection ◽  
Object Tracking ◽  
Detection Algorithm ◽  
Tracking Algorithm ◽  
Mesh Network ◽  
Detection Accuracy ◽  
Low Performance ◽  
Computational Resources ◽  
World Environment ◽  
Street Environment

The paper proposes an approach to object tracking for public street environments using dimensional based object detection algorithm. Besides the tracking functionality, the proposed algorithm improves the detection accuracy of the dimensional based object detection algorithm. The proposed tracking approach uses detection information obtained from multiple cameras which are structured as a mesh network. Conducted experiments performed in a real-world environment have shown 10 to 40 percent higher detection accuracy that has proved the proposed concept. The tracking algorithm requires negligible computational resources that make the algorithm especially applicable for low-performance Internet of things infrastructure.

Siamese Network Object Tracking Algorithm Combining Attention Mechanism and Correlation Filter Theory

2021 ◽  
Author(s):  
Xiuhua Hu ◽  
Huan Liu ◽  
Yuan Chen ◽  
Yan Hui ◽  
Yingyu Liang ◽  
...  
Keyword(s):  
Object Tracking ◽  
Attention Mechanism ◽  
Tracking Algorithm ◽  
Correlation Filter ◽  
Filter Model ◽  
Filter Theory ◽  
Search Area ◽  
Template Update ◽  
Object Appearance ◽  
Network Object

Aiming to solve the problem of tracking drift during movement, which was caused by the lack of discriminability of the feature information and the failure of a fixed template to adapt to the change of object appearance, the paper proposes an object tracking algorithm combining attention mechanism and correlation filter theory based on the framework of full convolutional Siamese neural networks. Firstly, the apparent information is processed by using the attention mechanism thought, where the object and search area features are optimized according to the spatial attention and channel attention module. At the same time, the cross-attention module is introduced to process the template branch and search area branch, respectively, which makes full use of the diversified context information of the search area. Then, the background perception correlation filter model with scale adaptation and learning rate adjustment is adopted into the model construction, using as a layer in the network model to realize the object template update. Finally, the optimal object location is determined according to the confidence map with similarity calculation. Experimental results show that the designed method in the paper can promote the object tracking performance under various challenging environments effectively; the success rate increases by 16.2%, and the accuracy rate increases by 16%.

Multiple Object Tracking Algorithm Based on Kernel Correlation Filter

2018 ◽  
Vol 55 (9) ◽  
pp. 091502
Author(s):  
周海英 Zhou Haiying ◽  
杨阳 Yang Yang ◽  
王守义 Wang Shouyi
Keyword(s):  
Object Tracking ◽  
Multiple Object Tracking ◽  
Tracking Algorithm ◽  
Correlation Filter ◽  
Multiple Object ◽  
Kernel Correlation

scholarly journals Deep Learning-Based Object Detection, Localisation and Tracking for Smart Wheelchair Healthcare Mobility

2020 ◽  
Vol 18 (1) ◽  
pp. 91
Author(s):  
Louis Lecrosnier ◽  
Redouane Khemmar ◽  
Nicolas Ragot ◽  
Benoit Decoux ◽  
Romain Rossi ◽  
...  
Keyword(s):  
Object Detection ◽  
Object Tracking ◽  
Indoor Environment ◽  
Distance Estimation ◽  
Depth Estimation ◽  
Detection Algorithm ◽  
Use Case ◽  
Detection Distance ◽  
Detection Depth ◽  
Sort Algorithm

This paper deals with the development of an Advanced Driver Assistance System (ADAS) for a smart electric wheelchair in order to improve the autonomy of disabled people. Our use case, built from a formal clinical study, is based on the detection, depth estimation, localization and tracking of objects in wheelchair’s indoor environment, namely: door and door handles. The aim of this work is to provide a perception layer to the wheelchair, enabling this way the detection of these keypoints in its immediate surrounding, and constructing of a short lifespan semantic map. Firstly, we present an adaptation of the YOLOv3 object detection algorithm to our use case. Then, we present our depth estimation approach using an Intel RealSense camera. Finally, as a third and last step of our approach, we present our 3D object tracking approach based on the SORT algorithm. In order to validate all the developments, we have carried out different experiments in a controlled indoor environment. Detection, distance estimation and object tracking are experimented using our own dataset, which includes doors and door handles.

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