Improved Target Signal Source Tracking and Extraction Method Based on Outdoor Visible Light Communication Using a Cam-Shift Algorithm and Kalman Filter
<p>Geometric model of an OVLC system and processing in the receiver.</p> "> Figure 2
<p>(<b>a</b>) The rolling shutter mechanism of the CMOS. (<b>b</b>) The target signal source LED area of the captured image of the CMOS sensor after a period of exposure.</p> "> Figure 3
<p>The process of obtaining the back-projection image.</p> "> Figure 4
<p>(<b>a</b>) Original image, (<b>b</b>) histogram of the H component of the target LED signal source, and (<b>c</b>) back-projection image, respectively.</p> "> Figure 5
<p>The process of predicting and updating Kalman filter.</p> "> Figure 6
<p>Detailed procession of the proposed tracking algorithm.</p> "> Figure 7
<p>Experimental system configuration. (<b>a</b>) Transmitter, (<b>b</b>) Receiver.</p> "> Figure 8
<p>The calculated coordinate value and the actual coordinate value of the target signal source LED: (<b>a</b>) tracking result of the target signal source LED, (<b>b</b>) Tracking result of the <span class="html-italic">x</span> coordinate, and (<b>c</b>) tracking result of the <span class="html-italic">y</span> coordinate.</p> "> Figure 8 Cont.
<p>The calculated coordinate value and the actual coordinate value of the target signal source LED: (<b>a</b>) tracking result of the target signal source LED, (<b>b</b>) Tracking result of the <span class="html-italic">x</span> coordinate, and (<b>c</b>) tracking result of the <span class="html-italic">y</span> coordinate.</p> "> Figure 9
<p>Tracking error of the target signal source LED: (<b>a</b>) total tracking error, and (<b>b</b>) tracking error of <span class="html-italic">x</span> and <span class="html-italic">y</span> coordinate.</p> "> Figure 10
<p>The cumulative distribution function (CDF) of the tracking error.</p> "> Figure 11
<p>Target signal source LED selection and tracking algorithm initializing without interference.</p> "> Figure 12
<p>Tracking performance of the proposed algorithm when occlusion occurs: (<b>a</b>) frame number: 14, (<b>b</b>) frame number 21, (<b>c</b>) frame number 34, and (<b>d</b>) frame number 47.</p> "> Figure 12 Cont.
<p>Tracking performance of the proposed algorithm when occlusion occurs: (<b>a</b>) frame number: 14, (<b>b</b>) frame number 21, (<b>c</b>) frame number 34, and (<b>d</b>) frame number 47.</p> "> Figure 13
<p>Tracking performance of the proposed algorithm under similar color interference and background interference: (<b>a</b>) frame number 63, (<b>b</b>) frame number 74, (<b>c</b>) frame number 86, and (<b>d</b>) frame number 91.</p> "> Figure 13 Cont.
<p>Tracking performance of the proposed algorithm under similar color interference and background interference: (<b>a</b>) frame number 63, (<b>b</b>) frame number 74, (<b>c</b>) frame number 86, and (<b>d</b>) frame number 91.</p> ">
Abstract
:1. Introduction
2. Theory
2.1. Outdoor Visible Light Communication
2.2. Traditional Cam-Shift Algorithm
2.2.1. Back Projection
- (1)
- Select the LED signal source area as the tracking target and calculate its color histogram of the H component in HSV color space.
- (2)
- Obtain the hue value (H component) of pixel in the image and find its corresponding interval according to .
- (3)
- Find the probability value of the corresponding interval in the histogram.
- (4)
- Use this probability value as the value of pixel of the back-projection.
- (5)
- Replace the value of each pixel in the image with its corresponding probability value.
2.2.2. Mean-Shift Iteration
- (1)
- Initialize the center and the size of the search window.
- (2)
- Calculate the center of mass of the search window:Calculate the zero moment and first moments and :Move the center of search window to the center of mass.
- (3)
- Exit the program when the moving distance of search window is less than a given threshold or reaches the maximum number of iterations; otherwise, return to step 2.
2.2.3. Cam-Shift Tracking
2.3. Kalman Filter
- (1)
- Initialize the Kalman filter: The state vector of the target signal source LED is a four-dimensional vector, including the coordinate of the center position of the target and the velocity component in the directions of and , shown in Equation (14). Initialize the state transition matrix , measurement matrix , covariance matrix of motion noise , and covariance matrix of measurement noise as:
- (2)
- Predict: Calculate the predicted state vector and prior covariance matrix of the current frame according to Equations (9) and (10).
- (3)
- Update: The measured value is given according to Equation (21), expressed with the central position coordinate of the target. The optimal estimated value can be obtained based on the predicted value and the measured value. Meanwhile, take the optimal estimated value as the target state of the current frame and make preparations for the next prediction.
- (4)
- Return to step 2 and continue the procedure until the end of the tracking process.
2.4. Improved Cam-Shift Algorithm
3. Experimental Setup and Result Analysis
3.1. Experimental Setup
3.2. Result and Analysis
3.2.1. Accuracy Performance
3.2.2. Robustness Performance
3.2.3. Real-Time Performance
4. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Camera Specifications | |
---|---|
Model | MV-U300 |
Spectral Response Range (nm) | ≈400–1030 |
Resolution | 800 × 600 |
Frame Rate (FPS) | 46 |
Dynamic Range (dB) | >61 |
Signal-to-noise Ratio (dB) | 43 |
Pixel (H × V) | 2048 × 1536 |
Pixel Size (μm²) | 3.2 × 3.2 |
Time of Exposure (ms) | 0.0556–683.8 |
Sensitivity | 1.0 V/lux–sec 550 nm |
Optical Filter | 650 nm Low Pass Optical Filter |
Type of Shutter | Electronic Rolling Shutter |
Acquisition Mode | Successive and Soft Trigger |
Working Temperatures (°C) | 0–50 |
Support Multiple Visual Software | OpenCV, LabView |
Support Multiple Systems | Vista, Win7, Win8, Win10 |
LED Specifications | |
Diameter of the LED (mm) | 150 |
Power of the LED (W) | 6 |
The half-power angles of LED (deg()) | 60 |
ROS Robot Specifications | |
Size (L × W × H) (mm3) Weight (+ SBC + Battery + Sensors) (kg) SBC (Single Board Computer) | 138 × 178 × 141 1 Raspberry Pi 3 Model B |
MCU | 32-bit ARM Cortex® -M7 with FPU (216 MHz, 462 DMIPS) |
Power connectors | 3.3 V/800 mA 5 V/4 A 12 V/1 A |
Power adapter (SMPS) | Input: 100–240 V, AC 50/60 Hz, 1.5 A @max Output: 12 V DC, 5 A |
Actuator | Dynamixel XL460-W250 |
PC connection | USB |
STM32 Development Specifications | |
CPU FLASH/SRAM Power supply port | STM32F407ZGT6, LQFP144 1024 K/192 K 5 V/3.3 V |
Drive Circuit Board Specifications | |
Drive chip | DD311 |
Drive current (A) | 0.1 |
Drive voltage (V) | 28 |
LED’s Category of Videos | Processing Time of Each Frame (s) |
---|---|
Green signal source LED | 0.042 |
Blue signal source LED Red signal source LED White signal source LED | 0.041 0.041 0.043 |
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Share and Cite
Huang, M.; Guan, W.; Fan, Z.; Chen, Z.; Li, J.; Chen, B. Improved Target Signal Source Tracking and Extraction Method Based on Outdoor Visible Light Communication Using a Cam-Shift Algorithm and Kalman Filter. Sensors 2018, 18, 4173. https://doi.org/10.3390/s18124173
Huang M, Guan W, Fan Z, Chen Z, Li J, Chen B. Improved Target Signal Source Tracking and Extraction Method Based on Outdoor Visible Light Communication Using a Cam-Shift Algorithm and Kalman Filter. Sensors. 2018; 18(12):4173. https://doi.org/10.3390/s18124173
Chicago/Turabian StyleHuang, Mouxiao, Weipeng Guan, Zhibo Fan, Zenghong Chen, Jingyi Li, and Bangdong Chen. 2018. "Improved Target Signal Source Tracking and Extraction Method Based on Outdoor Visible Light Communication Using a Cam-Shift Algorithm and Kalman Filter" Sensors 18, no. 12: 4173. https://doi.org/10.3390/s18124173