scholarly journals Real Time Vehicle Tracking Algorithm Based on Statistical Characteristics in HSI Color Model

2017 ◽  
Vol 10 (1) ◽  
pp. 59-70
Author(s):  
Pramod Sethy ◽  
Gaurav Singh ◽  
Karuna Lochab
Keyword(s):  
Real Time ◽  
Tracking Algorithm ◽  
Vehicle Tracking ◽  
Statistical Characteristics ◽  
Color Model ◽  
Hsi Color Model

A Multi-Vehicle Tracking Algorithm Based on QPSO Clustering Algorithm and Particle Position Matching Template

2013 ◽  
Vol 709 ◽  
pp. 413-416 ◽  
Author(s):  
Shao Jiang Liu ◽  
Wei Jian Mo ◽  
Zhi Ping Wan
Keyword(s):  
Real Time ◽  
Clustering Algorithm ◽  
Tracking Algorithm ◽  
Vehicle Tracking ◽  
Moving Vehicle ◽  
Particle Position ◽  
Moving Vehicles ◽  
Multiple Vehicles ◽  
Special Area ◽  
Image Separation

In this paper,we introduce a new tracking algorithm to detect and track multiple Vehicles in highway or special area. For the traditional vehicle tracking algorithm that can not be real-time tracking of vehicles or appear to tracking chaotic situation when most of the scene is in motion and component parts of vehicles are occluded. To overcome this problem, we use a background image separation method to separate the moving vehicle and the background and introduce a QPSO clustering algorithm for gathering together color particles of vehicles moving target,finally we through the particle position matching template to achieve target matching tracking.In an experimental evaluation, we show that algorithm through processing the monitoring image of moving vehicles to show tracking information of moving vehicle in the real-time.

Field Assessment of Camera Based Drilling Dynamics

2021 ◽  
Author(s):  
Alexis Koulidis ◽  
Mohamed Abdullatif ◽  
Ahmed Galal Abdel-Kader ◽  
Mohammed-ilies Ayachi ◽  
Shehab Ahmed ◽  
...  
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Real Time ◽  
Tracking Algorithm ◽  
Interest Points ◽  
Stick Slip ◽  
Detection Algorithms ◽  
Point Tracking ◽  
Surface Data ◽  
Field Assessment

Abstract Surface data measurement and analysis are an established mean of detecting drillstring low-frequency torsional vibration or stick-slip. The industry has also developed models that link surface torque and downhole drill bit rotational speed. Cameras provide an alternative noninvasive approach to existing wired/wireless sensors used to gather such surface data. The results of a preliminary field assessment of drilling dynamics utilizing camera-based drillstring monitoring are presented in this work. Detection and timing of events from the video are performed using computer vision techniques and object detection algorithms. A real-time interest point tracker utilizing homography estimation and sparse optical flow point tracking is deployed. We use a fully convolutional deep neural network trained to detect interest points and compute their accompanying descriptors. The detected points and descriptors are matched across video sequences and used for drillstring rotation detection and speed estimation. When the drillstring's vibration is invisible to the naked eye, the point tracking algorithm is preceded with a motion amplification function based on another deep convolutional neural network. We have clearly demonstrated the potential of camera-based noninvasive approaches to surface drillstring dynamics data acquisition and analysis. Through the application of real-time object detection algorithms on rig video feed, surface events were detected and timed. We were also able to estimate drillstring rotary speed and motion profile. Torsional drillstring modes can be identified and correlated with drilling parameters and bottomhole assembly design. A novel vibration array sensing approach based on a multi-point tracking algorithm is also proposed. A vibration threshold setting was utilized to enable an additional motion amplification function providing seamless assessment for multi-scale vibration measurement. Cameras were typically devices to acquire images/videos for offline automated assessment (recently) or online manual monitoring (mainly), this work has shown how fog/edge computing makes it possible for these cameras to be "conscious" and "intelligent," hence play a critical role in automation/digitalization of drilling rigs. We showcase their preliminary application as drilling dynamics and rig operations sensors in this work. Cameras are an ideal sensor for a drilling environment since they can be installed anywhere on a rig to perform large-scale live video analytics on drilling processes.

scholarly journals A Low-Light Sensor Image Enhancement Algorithm Based on HSI Color Model

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3583 ◽  
Author(s):  
Shiping Ma ◽  
Hongqiang Ma ◽  
Yuelei Xu ◽  
Shuai Li ◽  
Chao Lv ◽  
...  
Keyword(s):  
Image Enhancement ◽  
Color Space ◽  
Color Model ◽  
Low Light ◽  
Image Brightness ◽  
Low Contrast ◽  
Light Sensor ◽  
Hsi Color Model ◽  
Art Research ◽  
Enhancement Algorithm

Images captured by sensors in unpleasant environment like low illumination condition are usually degraded, which means low visibility, low brightness, and low contrast. In order to improve this kind of images, in this paper, a low-light sensor image enhancement algorithm based on HSI color model is proposed. At first, we propose a dataset generation method based on the Retinex model to overcome the shortage of sample data. Then, the original low-light image is transformed from RGB to HSI color space. The segmentation exponential method is used to process the saturation (S) and the specially designed Deep Convolutional Neural Network is applied to enhance the intensity component (I). At the end, we back into the original RGB space to get the final improved image. Experimental results show that the proposed algorithm not only enhances the image brightness and contrast significantly, but also avoids color distortion and over-enhancement in comparison with some other state-of-the-art research papers. So, it effectively improves the quality of sensor images.

Sign in / Sign up

Export Citation Format

Share Document