S16: SkyNet
Contents
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SkyNet
Abstract
SkyNet is a tracking camera mount that follows a given target using computer vision technologies. The system utilizes two stepper motors that are controlled by inputs given from a Raspberry Pi 3. The Raspberry Pi 3 utilizes the OpenCV open source library to calculate the deviation of a tracked object from the center of its view. System then control the motors to correct the camera position such that the target is aligned to the center of the video. The mount will be able to hold any standard 5-inch phone or small point and shoot for video recording. The completed project was able to hold any smartphone and small point and shoot cameras. The system allowed for limited pitch rotation and unlimited yaw rotation. The tracking solution used an OpenCV method called haar cascade. The tracking was adequate enough to track a slow moving target and had issues with backgrounds of similar color to the tracked person.
Objectives & Introduction
The objective of this project was to create a tracking camera mount that kept a target within the center of the camera's view at all times. To accomplish this a custom frame was created to house a stepper motor system and an OpenCV based tracking solution. The project has a custom frame that was modeled in a CAD software called Fusion360 and printed using a 3D printer. The stepper motor system consists of two motors for yaw and pitch rotation. The OpenCV tracking software was run on a Raspberry Pi 3 that used the Raspberry Pi camera as an input to the software.
Team Members & Responsibilities
There were four team members in this project and two teams of two were created for each part of the project. One team was focused on creating an OpenCV based tracking solution. The other team was focused on creating a physical system for the tracking software to control. The breakdown of the teams is shown below.
OpenCV tracking solution
- Steven Hwu
- Jason Tran
Motor System and frame
- Andrew Herbst
- Vince Ly
Motor System
The objective of the motor system was to create a system that allowed yaw and pitch rotation. The system was designed to be place on a table or a tripod at chest level. The frame that held the system was designed to use the least amount of material and allow for unobstructed rotation on either axis. To accomplish this stepper motors will be placed horizontally and vertically. Stepper motors are ideal for an accurate system like this because of the simple control scheme. Also stepper motors are very accurate because they rotate in steps and can micro-step to decrease the jitter of motor movement. A Raspberry Pi 3 was the processing platform chosen. This platform runs linux and had the bare minimum performance to run an OpenCV tracking solution. To power the system a power circuit was created to power the motors and the Raspberry Pi. Ideally the system would include a battery but due to lack of time a power adapter supplied power to the system. This restricted the movement of rotation on the horizontal access.
The bare minimum requirements for the motor system is summarized below.
Specifications
- Allow for yaw and pitch rotation
- Small footprint of the entire system frame
- A compute platform capable of running OpenCV tracking code
- Power circuit to power two stepper motors/compute platform
Tracking system
The purpose of the tracking system is find the position of a target in a video source. A video source is a source of visual data in the form of a video file or a camera; in this case, SkyNet obtains its visual data from a camera. To analyze visual data, images are read from a video source in the form matrices. To track a target, it must be present and identified in an image/frame of a video source.
There are 3 candidate methods to identify and find the position of a target:
- Analyze the color space of an image
- Provide a sample image of the desired target and find a matches in an image
- Use an object classifier as a representation of an object, then look for that object in an image
For this project, a target is identified using an object classifier in combination with Haar Cascade object detection method. Using a frontal face object classifier, the target to be tracked is the face of a person.
Schedule
Show a simple table or figures that show your scheduled as planned before you started working on the project. Then in another table column, write down the actual schedule so that readers can see the planned vs. actual goals. The point of the schedule is for readers to assess how to pace themselves if they are doing a similar project.
| Week# | Due Date | Task | Completed | Notes |
|---|---|---|---|---|
| 1 | 3/29 | - Create Parts list and place order (Motors, Cameras, etc.)
- Compile OpenCV C++ code and run examples on Raspberry Pi 3 |
Completed | - Ordered parts on 3/27
- OpenCV library is building on both development PCs (Steven/Jason) TODO: - Run OpenCV on Raspberry Pi 3 |
| 2 | 4/5 | - Create motorized unit
- Create the CAD model for 3D printing - Create the breakout board for the motor controller - Be able to track an object in frame (Highlight object) |
Completed | - Successfully tracked an object in HSV color space.
- Successfully tracked human w/ various other objects. - Human tracking not enough to track one person, trying combination of HSV tracking - Initial CAD model created w/ Autodesk Fusion 360 - Created PCB for TI chip, looking for fab houses |
| 3 | 4/12 | - Test various motors for behavior/control
- Extrapolate movement of object |
Completed | - Looking into Stepper vs Servo vs Brushless for optimal control/smoothness
- Looking into face tracking as option - Researching how to narrow down human tracking to 1 specific person. |
| 4 | 4/19 | - Sync-up on how to command motors (scaling, etc.)
- Create API interface to control motors - Create communication tasks to control motors |
- Moving forward with Stepper motor implementation
- Using 12V wall adapter as power source, if time permits get a battery sytem - Aim to finish the frame by this weekend, Horizontal part prints on Saturday and Veritical part prints on Sunday - Developing HSV as fallback tracking - Moving forward with Machine learning implementation | |
| 5 | 4/26 | - Integration of control system and motor unit | Completed | - Push back integration to 5/3
- Tested stepper motor drivers |
| 6 | 5/3 | - Created motor system circuit design/wiring layout | Completed | - Pushed integration to 5/10 |
| 7 | 5/10 | - Finish motor system wiring
- Integration between motor system and tracking system |
Completed | - Pushed back optimization till 5/17 |
| 7 | 5/17 | - Motor system Optimization | Completed | - Finished motor system optimization
- tracking optimization on 5/22 |
| 7 | 5/22 | - tracking optimization | Completed | - Tracking with backgrounds similar color to person causes issues
- Turn on smartphone light to increase contrast between user and background - Motor system extremely hot, might have to do with current draw |
Parts List & Cost
ECU:
RaspBerry Pi 3 Rev B ~$40
- https://www.raspberrypi.org/products/raspberry-pi-3-model-b/
Tracking Camera:
Raspberry Pi Camera Board Module ~$20
- http://www.amazon.com/Raspberry-5MP-Camera-Board-Module/dp/B00E1GGE40
Stepper Motors:
Nema 14 Stepper Motor (Pitch Motor)~$24
- http://www.amazon.com/0-9deg-Stepper-Bipolar-36x19-5mm-4-wires/dp/B00W91K3T6/ref=pd_sim_60_2?ie=UTF8&dpID=41CZcFZwJJL&dpSrc=sims&preST=_AC_UL160_SR160%2C160_&refRID=0ZZRP157RFT10QDHHKN4
Stepping Motor Nema 17 Stepping Motor (Yaw Motor) ~$13
- http://www.amazon.com/Stepping-Motor-26Ncm-36-8oz-Printer/dp/B00PNEQ9T4/ref=sr_1_4?s=industrial&ie=UTF8&qid=1463981462&sr=1-4&keywords=stepper+motor+nema+17
Controllers:
x2 DRV8825 Stepper Motor Driver Carrier, High Current ~$10
- https://www.pololu.com/product/2133
Phone mount:
x1 Tripod phone mount ~$5
Whole Enclosure:
3D printed
Total: ~$123
Design & Implementation
Hardware Design
Frame
A custom frame was created to hold the motors and cameras in place. The frame was created in Fusion360, a CAD software for created 3D models. The design went through various changes and after two weeks a final design was created. The design was created with the intention to be as small as possible. Since the system used a large stepper motor for the horizontal rotation the frame ended up being very tall. One possible change in the future would be to add more functionality to the lower portion of the design.
The frame design was created in four separate parts: horizontal frame, vertical frame, base, and base plate. The horizontal frame housed all of the electronics of the system. This included the Raspberry Pi 3, motor drivers, power circuit, pitch stepper motor, and yaw stepper motor. The vertical frame held the tracking camera and user camera in place. The vertical frame was attached to the pitch stepper motor. The base allowed the system to stand on its own and served as a intermediate connector to a possible tripod mount. The base plate was designed to cover the bottom of the base and allowed the system to be held by a tripod head.
Once the 3D model of the frame was completed it was printed using a 3D printer. The horizontal frame took the longest to print by taking 20 hours to complete. The vertical frame was a fraction of the time taking only 3 hours to print. The fastest part to print was the base which took about 2 hours to print because of its symmetrical shape. In total it took roughly 25 hours to print the entire frame for the project.
Power Unit
A +12V supply was needed to power both of the motor drivers. A +12V wall adapter was used along with a pair of coupling capacitors to run power to the DRV8825 IC's. In addition, a +5V supply was required to power the Raspberry Pi 3 board, which controls the motor drivers over GPIO. To step down the 12 volts from the wall adapter, we used a 5V switching regulator because it could handle the heat and power capacity that would be required when running the motors.
Motor Driver
The TI DRV8825 IC is capable of driving a bipolar stepper motor, so two of these chips were implemented for the pitch and yaw motors. The controller was powered with a 12V supply, as shown in the figure. This specific motor driver was chosen primarily because of its simple STEP/DIR interface. The driver would receive a direction signal along with a PWM signal. The frequency of the PWM signal dictates the speed at which the stepper motor would turn in the direction specified by the DIR pin. The motor driver also had microstepping capabilities so that we could coordinate the steps per rotation of each motor to deliver the maximum smoothness while turning the motor. The breakout board that carried the motor driver allowed for easy implementation and connection to the Raspberry Pi and PWM driver.
PWM Driver
Because the Raspberry Pi 3 only has one hardware PWM output pin, an external PWM driver was chosen to drive the PWM signals that controlled the two stepper motors. The PCA9685 16-channel PWM controller was chosen as the interface between the microcontroller and the two stepper motors. The PWM controller communicates with the Raspberry Pi over the I2C bus with just two wires. With this device, the PWM frequency could be adjusted between 24Hz and 1526 Hz, which was an acceptable range for the application of this project. The frequency was set by modifying the register that dictated the frequency prescaler in the equation in figure 4.
Hardware Interface
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Motor System Class
In order to communicate from the higher-level OpenCV motion tracking module to the lower-level motors, a Motor System class was developed in the intermediate layer. The Motor System class was developed with various functions that the motion tracking module would call upon to control the individual motors' movement. A summary of the basic functions the Motor System provided are below. The main purpose of the Motor System is to pass the speed and direction commanded by the OpenCV module to the individual motors and also to determine the motor step size that would produce the most refined movement of the motors.
// Motor System Class
void power_off(void);
void power_on(void);
/**
* Sets the speed for both the yaw_motor and pitch motor
*
* @param x_speed speed of horizontal motor
* @param y_speed speed of vertical motor
*/
void set_x_y_speed(float x_speed, float y_speed);
/**
* Checks if either system is faulted
*
* @return true if either motor is faulted
*/
bool is_faulted();
/*
* Sets motor step size for maximum smoothness given minimum rotation time
*/
void set_smoothness();
The Motor System is also responsible through its constructor for initializing the pitch motor and yaw motor by assigning each GPIO pin to its corresponding function in the Driver class.
// Motor Initialization:
pitch_motor = new Motor_driver(PITCH_PHYSICAL_STEPS, PWM_0, PIN_2, PIN_3, PIN_21, PIN_22, PIN_23, PIN_24, PIN_0);
yaw_motor = new Motor_driver(YAW_PHYSICAL_STEPS, PWM_1, PIN_4, PIN_5, PIN_6, PIN_25, PIN_27, PIN_28, PIN_1);
Motor Driver Class
The Motor Driver class has two instances, one for each motor, which is passed information such as speed and direction from the Motor System functions. Below is a list of the important functions available to the Motor Driver, the most vital being the rotate() function which parses the x and y speed information from the Motor System class and sets the corresponding DIR pin and sets the frequency of the PWM signal to send to the motor to control speed.
// Motor Driver Class
/**
* Sets the rotation of the motor can either be
* NEGATIVE, POSITIVE, or HOLD
*
* @param motion specifies the type of motion
* @param speed Sets the speed of the motor, 0 <= speed >= 1000
*/
void rotate(motion_e motion, int speed);
/**
* Sets the step size of the motor
*
* @param step_size_e step size for motor
*/
void set_step_size(step_size_e step_size);
/**
* Sets the direction of the motor positive or negative
*
* @param motion direction to move
*/
void set_dir(motion_e motion);
/**
* Holds the current position motor is at, used for speed == 0
*
*/
void hold_position(void);
When the motors are initialized, the constructor carries out the following tasks:
1. Set steps per rotation 2. Set current motion to HOLD 3. Set step size to 1 4. Set current speed to zero 5. Initialize the control pins of the motor driver to correspond to the GPIO pins of the Raspberry Pi
PWM Driver
The Raspberry Pi communicates with the PCA9685 PWM driver over the I2C bus through two wires, SDA and SCL. The microcontroller can manipulate the motor drivers by writing to different control registers on the PCA9685 in order to configure the output PWM and duty cycle to the motor drivers. For example, the sequence to set an output PWM frequency is as follows:
1. Check that the requested frequency is within the boundary (24-1526 Hz) and clamp value if necessary 2. Calculate prescaler based on requested frequency using formula from figure 4 3. Set sleep mode bit by writing 0x10 to Mode 1 address 0x00 4. Write the calculated prescaler value to the prescaler register at address 0xFE 5. Clear the sleep bit by writing 0x00 to Mode 1 address 0x00
Software Design
Implementation
Tracking Application using Haar Cascade Object Detection
The top level software using Haar Cascade object detection consists of 3 major steps:
- Initialization
- Create a classifier object, then load a classifier file
- A classifier is an XML file that describes a particular object, for example, the full body of a person.
- Initialize the video source
- The video source is an object that represents the video capture device, for example, a USB camera. Real-world visual data as a matrix can be obtained from this object.
- Create a classifier object, then load a classifier file
- Capture an instance from the video source (also known as a frame), then perform object detection on it
- Object detection is performed by searching the given frame for objects that match the previously loaded classifier file.
- After analyzing, a list of found targets is returned; found target are represented as Rectangle objects.
- Determine if the tripod’s camera needs to be rotated based on the target’s location in the frame.
- For each detected target in the found list, determine whether the tripod should pan or tilt.
- Currently, the tripod has the capability to pan/tilt in the following directions:
- Left
- Right
- Up
- Down
- If the panning/tilting action is required, activate the appropriate stepper motors (x-axis or y-axis)
Testing & Technical Challenges
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My Issue #1
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Conclusion
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Project Video
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Project Source Code
References
Acknowledgement
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References Used
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Appendix
Meeting notes
| Date | Meeting Notes |
|---|---|
| 3/27 | Tracking:
C++ for OpenCV
Have target stand dead center and let the PC choose what to target
- Green for go
- red for lost
Graceful halt, error compensation in OpenCV layer
Motor System: API for % speed for x and y axis x_axis_speed(int speed_percent) y_axis_speed(int speed_percent) "Dumb motor system" should not know about error or destination, only knows how fast to go in a direction Enclosure: Visible framework 3D printing Autodesk Fusion 360 |
| 4/3 | +++++++ Meetings Notes ++++++++
PCB Design - Completed Design for PCB - Initial quote was 18 per board - Looking into the price and seeing other fab house prices - Aiming to get PCB in 2 weeks time Motor controller - Going to use TI controller as base - Fallback is Servo Motors - Motors used in robotic arms? slow and percise - Will be using EVM from TI to test out TI behavior - Looking into other controllers to get desired result L6234, SPWM signals (Sine-wave PWM) CAD Frame - Re-adjust Raspberry Pi Cubby on Horizontal Frame - Add more support to Horizontal Frame arms - Aiming to get test print by next meeting, test durability. OpenCV - Trained model for people detection already exists
- How to differentiate a person? Premade function gives back a list of everyone
- We CAN detect people, We need to find out how to narrrow down the targets
- Use rectangles to get coordinates combined with HSV
- Combine coordinates, analyze with HSV
- HAR face tracking
- possibly can single out a target
|
| 4/10 | +++++++ Meetings Notes ++++++++
Hardware Side: Project Frame
- Successfully printed out Horizontal frame, approximately 9 hours to complete
- Used a SLA converter to get SLA file from 3D model in Fusion 360
- Used a slicer tool to create a sliced version of the model to use in the 3D printer
- Successfully drove a motor slow
- Used method detailed in this article http://www.berryjam.eu/2015/04/driving-bldc-gimbals-at-super-slow-speeds-with-arduino/
Raspberry Pi PWM
- Found two libraries to implement PWM control
Circuit diagram
- Started on full circuit diagram
Tracking: New ways of tracking
- Used frame difference tracking
- Pattern tracking
Template Matching and frame difference
- Look for frame differences and then choose objects based on the different objects in a frame
- Use template matching to then track the object that was choosen
Raspberry Pi Camera implementation
- Developed on laptop first and then moved to Raspberry Pi
- Heating issues on the Raspberry Pi
- Really bad performance capture at 20-30 frames
- Will get metrics of performance in next run
- Reduce the
Template Matching
- It can dictate anything as an object
- car, hand, etc.
- We have to select it to target
- Fast moving objects were still trackable
|
| 4/17 | +++++++ Meetings Notes ++++++++
Critical Meeting Hardware Side: Stepper motor control
- Accurate because of the way the motor is built
- Motors can be bought at various steps per rotation
- Microstepping can increase the amount of steps per rotation
Why switch from brushless motors?
- Brushless motor control is too inaccurate for camera control
- Voltage/current draw too large while using brushless
Power circuit
- Circuit to supply the power to the system
- The motors themselves have a max draw about 1 amp
- Raspberry PI needs minimum 5V and at max 1A
- Development phase use a 12V 3A wall adapter
- If time permits run system on battery power
Tracking: Template Matching inadequate
- Takes too much processing power
Machine Learning Training
- Offline training
- Online training (Real time training)
Fallback HSV tracking
- Will develop until it can be feasibly used
Min spec
- Tracking a student walking across the classroom
|
