scholarly journals MLSS-VO: A Multi-Level Scale Stabilizer with Self-Supervised Features for Monocular Visual Odometry in Target Tracking

Electronics ◽  
2022 ◽  
Vol 11 (2) ◽  
pp. 223
Author(s):  
Zihao Wang ◽  
Sen Yang ◽  
Mengji Shi ◽  
Kaiyu Qin
Keyword(s):  
Target Tracking ◽  
Feature Matching ◽  
Traditional Approach ◽  
Visual Odometry ◽  
Feature Point ◽  
Localization Algorithm ◽  
Current Frame ◽  
Scale Uncertainty ◽  
Multi Level ◽  
Level Scale

In this study, a multi-level scale stabilizer intended for visual odometry (MLSS-VO) combined with a self-supervised feature matching method is proposed to address the scale uncertainty and scale drift encountered in the field of monocular visual odometry. Firstly, the architecture of an instance-level recognition model is adopted to propose a feature matching model based on a Siamese neural network. Combined with the traditional approach to feature point extraction, the feature baselines on different levels are extracted, and then treated as a reference for estimating the motion scale of the camera. On this basis, the size of the target in the tracking task is taken as the top-level feature baseline, while the motion matrix parameters as obtained by the original visual odometry of the feature point method are used to solve the real motion scale of the current frame. The multi-level feature baselines are solved to update the motion scale while reducing the scale drift. Finally, the spatial target localization algorithm and the MLSS-VO are applied to propose a framework intended for the tracking of target on the mobile platform. According to the experimental results, the root mean square error (RMSE) of localization is less than 3.87 cm, and the RMSE of target tracking is less than 4.97 cm, which demonstrates that the MLSS-VO method based on the target tracking scene is effective in resolving scale uncertainty and restricting scale drift, so as to ensure the spatial positioning and tracking of the target.

scholarly journals Research on Target Tracking Algorithm Based on Siamese Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Haibo Pang ◽  
Qi Xuan ◽  
Meiqin Xie ◽  
Chengming Liu ◽  
Zhanbo Li
Keyword(s):  
Neural Network ◽  
Target Tracking ◽  
Evaluation Criteria ◽  
Attention Mechanism ◽  
Tracking Algorithm ◽  
Temporal Attention ◽  
Tracking Accuracy ◽  
Current Frame ◽  
Tracking Process ◽  
Recognition Ability

Target tracking is a significant topic in the field of computer vision. In this paper, the target tracking algorithm based on deep Siamese network is studied. Aiming at the situation that the tracking process is not robust, such as drift or miss the target, the tracking accuracy and robustness of the algorithm are improved by improving the feature extraction part and online update part. This paper adds SE-block and temporal attention mechanism (TAM) to the framework of Siamese neural network. SE-block can refine and extract features; different channels are given different weights according to their importance which can improve the discrimination of the network and the recognition ability of the tracker. Temporal attention mechanism can update the target state by adjusting the weights of samples at current frame and historical frame to solve the model drift caused by the existence of similar background. We use cross-entropy loss to distinguish the targets in different sequences so that their distance in the feature domains is longer and the features are easier to identify. We train and test the network on three benchmarks and compare with several state-of-the-art tracking methods. The experimental results demonstrate that the algorithm proposed is superior to other methods in tracking effect diagram and evaluation criteria. The proposed algorithm can solve the occlusion problem effectively while ensuring the real-time performance in the process of tracking.

Multiple Objects Tracking Based on Mixture Features

2014 ◽  
Vol 945-949 ◽  
pp. 1869-1874
Author(s):  
Dong Mei Li ◽  
Tao Li
Keyword(s):  
Target Tracking ◽  
Moving Objects ◽  
Coincidence Degree ◽  
Multiple Target Tracking ◽  
Multiple Target ◽  
Multiple Objects ◽  
Current Frame ◽  
Complex Scenes ◽  
Objects Tracking ◽  
Tracking Object

For multiple objects tracking in complex scenes, this paper proposes a new tracking algorithm for multiple moving objects. The algorithm makes likelihood calculation by using new DG_CENTRIST feature and color feature, and then calculates the overlapping ratio of the tracking object and the object in the current frame using coincidence degree to measure the occlusion. The algorithm has good robustness and stability, and the experiment results show that this method can effectively improve the accuracy of the multiple target tracking.

Feature Matching Based on Triangle Guidance and Constraints

2018 ◽  
Vol 32 (08) ◽  
pp. 1855014 ◽  
Author(s):  
Hongmin Liu ◽  
Hongya Zhang ◽  
Zhiheng Wang ◽  
Yiming Zheng
Keyword(s):  
Feature Matching ◽  
High Accuracy ◽  
Geometric Constraints ◽  
Feature Point ◽  
Feature Points ◽  
Matching Method ◽  
Shared Feature ◽  
Triangular Structure ◽  
Circle Center ◽  
Image Transformations

For images with distortions or repetitive patterns, the existing matching methods usually work well just on one of the two kinds of images. In this paper, we present novel triangle guidance and constraints (TGC)-based feature matching method, which can achieve good results on both kinds of images. We first extract stable matched feature points and combine these points into triangles as the initial matched triangles, and triangles combined by feature points are as the candidates to be matched. Then, triangle guidance based on the connection relationship via the shared feature point between the matched triangles and the candidates is defined to find the potential matching triangles. Triangle constraints, specially the location of a vertex relative to the inscribed circle center of the triangle, the scale represented by the ratio of corresponding side lengths of two matching triangles and the included angles between the sides of two triangles with connection relationship, are subsequently used to verify the potential matches and obtain the correct ones. Comparative experiments show that the proposed TGC can increase the number of the matched points with high accuracy under various image transformations, especially more effective on images with distortions or repetitive patterns due to the fact that the triangular structure are not only stable to image transformations but also provides more geometric constraints.

Feature Matching With an Adaptive Optical Sensor in a Ground Target Tracking System

2015 ◽  
Vol 15 (1) ◽  
pp. 510-519 ◽  
Author(s):  
Burak Uzkent ◽  
Matthew J. Hoffman ◽  
Anthony Vodacek ◽  
Bin Chen
Keyword(s):  
Target Tracking ◽  
Optical Sensor ◽  
Feature Matching ◽  
Tracking System ◽  
Ground Target ◽  
Ground Target Tracking

Algorithm for Target Tracking Using Passive Radar

2012 ◽  
Vol 58 (4) ◽  
pp. 345-350 ◽  
Author(s):  
Mateusz Malanowski
Keyword(s):  
Target Tracking ◽  
Real Data ◽  
Tracking Algorithm ◽  
Target Localization ◽  
Passive Radar ◽  
Localization Algorithm ◽  
Two Stage ◽  
Cartesian Coordinates ◽  
The Real ◽  
University Of Technology

Abstract In the paper the problem of target tracking in passive radar is addressed. Passive radar measures bistatic parameters of a target: bistatic range and bistatic velocity. The aim of the tracking algorithm is to convert the bistatic measurements into Cartesian coordinates. In the paper a two-stage tracking algorithm is presented, using bistatic and Cartesian tracking. In addition, a target localization algorithm is applied to initialize Cartesian tracks from bistatic measurements. The tracking algorithm is tested using simulated and real data. The real data were obtained from an FM-based passive radar called PaRaDe, developed at Warsaw University of Technology.

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