scholarly journals A Robust Visual Tracking Method for Unmanned Mobile Systems

2019 ◽  
Vol 141 (7) ◽  
Author(s):  
Xiongfeng Yi ◽  
Zheng Chen
Keyword(s):  
Visual Tracking ◽  
Water Surface ◽  
Mobile Systems ◽  
Light Reflection ◽  
Image Process ◽  
Water Tank ◽  
Complex Environments ◽  
Tracking Method ◽  
Real Time Tracking ◽  
Image Pixels

This paper introduces a robust visual tracking of objects in complex environments with blocking obstacles and light reflection noises. This visual tracking method utilizes a transfer matrix to project image pixels back to real-world coordinates. During the image process, a color and shape test is used to recognize the object and a vector is used to represent the object, which contains the information of orientation and body length of the object. If the object is partially blocked by the obstacles or the reflection from the water surface, the vector predicts the position of the object. During the real-time tracking, a Kalman filter is used to optimize the result. To validate the method, the visual tracking algorithm was tested by tracking a submarine and a fish on the water surface of a water tank, above which three pieces of blur glass were blocking obstacles between the camera and the object. By using this method, the interference from the reflection of the side glass and the fluctuation of the water surface can be also avoided.

A Robust and Optimal Visual Tracking Method With Blocking Obstacles and Water Reflection Noises

2018 ◽  
Author(s):  
Xiongfeng Yi ◽  
Zheng Chen
Keyword(s):  
Visual Tracking ◽  
Water Surface ◽  
Optimal Algorithm ◽  
Dimensional Space ◽  
The Body ◽  
Moving Object ◽  
Tracking Method ◽  
The Real ◽  
Tracking Process ◽  
Image Pixels

Image-based tracking has been widely used to obtain the position and velocity information of a moving object in a 2-dimensional or 3-dimensional space. However, the tracking process is always affected by reflection noises and blocking obstacles in the environment. This paper provides a robust and optimal algorithm for tracking a moving object on the surface of water. First, we create a matrix to project the image pixels back to the real world coordinate. Second, color and shape tests are used to recognize the object and a vector is used to represent the object. If the object is partially blocked by the obstacles or the reflection from the water surface, the vector is used to predict the position of the body. In the real-time tracking, a Kalman filter is used to optimize the prediction. We tested our algorithm by tracking a submarine on the water surface of a tank. Experimental results show that the visual tracking method is robust to reflection noises and blocking obstacles.

DBN Models for Visual Tracking and Prediction

2007 ◽  
pp. 176-193
Author(s):  
Qian Diao ◽  
Jianye Lu ◽  
Wei Hu ◽  
Yimin Zhang ◽  
Gary Bradski
Keyword(s):  
Visual Tracking ◽  
General Framework ◽  
Broad Class ◽  
Probability Distributions ◽  
Prediction Method ◽  
Dynamic Bayesian Networks ◽  
Time Series Models ◽  
Complex Environments ◽  
Inference Algorithms ◽  
Background Clutter

In a visual tracking task, the object may exhibit rich dynamic behavior in complex environments that can corrupt target observations via background clutter and occlusion. Such dynamics and background induce nonlinear, nonGaussian and multimodal observation densities. These densities are difficult to model with traditional methods such as Kalman filter models (KFMs) due to their Gaussian assumptions. Dynamic Bayesian networks (DBNs) provide a more general framework in which to solve these problems. DBNs generalize KFMs by allowing arbitrary probability distributions, not just (unimodal) linear-Gaussian. Under the DBN umbrella, a broad class of learning and inference algorithms for time-series models can be used in visual tracking. Furthermore, DBNs provide a natural way to combine multiple vision cues. In this chapter, we describe some DBN models for tracking in nonlinear, nonGaussian and multimodal situations, and present a prediction method to assist feature extraction part by making a hypothesis for the new observations.

scholarly journals Extraction of 3D Features from Complex Environments in Visual Tracking Applications

2007 ◽  
Author(s):  
Marta Marron ◽  
Juan Carlos Garcia ◽  
Miguel Angel Sotelo ◽  
Daniel Pizarro Perez ◽  
Ignacio Bravo Munoz
Keyword(s):  
Visual Tracking ◽  
Complex Environments

scholarly journals HKSiamFC: Visual-Tracking Framework Using Prior Information Provided by Staple and Kalman Filter

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2137 ◽  
Author(s):  
Chenpu Li ◽  
Qianjian Xing ◽  
Zhenguo Ma
Keyword(s):  
Kalman Filter ◽  
Visual Tracking ◽  
Prior Information ◽  
State Of The Art ◽  
The Other ◽  
Robust Tracking ◽  
Similarity Learning ◽  
Complex Environments ◽  
Color Information ◽  
Learning Problem

In the field of visual tracking, trackers based on a convolutional neural network (CNN) have had significant achievements. The fully-convolutional Siamese (SiamFC) tracker is a typical representation of these CNN trackers and has attracted much attention. It models visual tracking as a similarity-learning problem. However, experiments showed that SiamFC was not so robust in some complex environments. This may be because the tracker lacked enough prior information about the target. Inspired by the key idea of a Staple tracker and Kalman filter, we constructed two more models to help compensate for SiamFC’s disadvantages. One model contained the target’s prior color information, and the other the target’s prior trajectory information. With these two models, we design a novel and robust tracking framework on the basis of SiamFC. We call it Histogram–Kalman SiamFC (HKSiamFC). We also evaluated HKSiamFC tracker’s performance on dataset of the online object tracking benchmark (OTB) and Temple Color (TC128), and it showed quite competitive performance when compared with the baseline tracker and several other state-of-the-art trackers.

A novel visual tracking method using bat algorithm

2016 ◽  
Vol 177 ◽  
pp. 612-619 ◽  
Author(s):  
Ming-Liang Gao ◽  
Jin Shen ◽  
Li-Ju Yin ◽  
Wei Liu ◽  
Guo-Feng Zou ◽  
...  
Keyword(s):  
Visual Tracking ◽  
Bat Algorithm ◽  
Tracking Method

Numerical Modeling of the Oil Spill Trajectory and Real-Time Tracking Using Intelligent Swarm Robots

2013 ◽  
Vol 446-447 ◽  
pp. 1261-1265 ◽  
Author(s):  
Mohsen Pashna ◽  
Rubiyah Yusof ◽  
Zool H. Ismail
Keyword(s):  
Numerical Modeling ◽  
Numerical Model ◽  
Crude Oil ◽  
Oil Spill ◽  
Water Surface ◽  
Sea Surface ◽  
Sea Waves ◽  
Robot Locomotion ◽  
Swarm Robots ◽  
Real Time Tracking

An oil spill is discharge of fluid petroleum such as crude oil or its by-product derivations such as diesel and gasoline on the water surface. In this paper, a numerical model of the oil spill has been introduced as a simulation of releasing oil on the sea surface. Meantime, the influence of sea waves and wind has been considered and shown. Moreover, a swarm of robots is engaged in order to track the spreading boundaries of the slicked oil, so that a novel schedule of robot locomotion is presented, based on the online sharing information in the flock network. Therefore, the swarm of robots tracks the oil spill margins intelligently and successfully.

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