Drowsiness and attention monitor for driving or handling heavy machinery. Also detects objects at the blind spot via CV and the Jetson Nano.
Introduction:
We will be tackling the problem of drowsiness when handling or performing tasks such as driving or handling heavy machinery and the blind spot when driving.
The Center for Disease Control and Prevention (CDC) says that 35% of American drivers sleep less than the recommended minimum of seven hours a day. It mainly affects attention when performing any task and in the long term, it can affect health permanently.
According to a report by the WHO (World Health Organization) (2), falling asleep while driving is one of the leading causes of traffic accidents. Up to 24% of accidents are caused by falling asleep, and according to the DMV USA (Department of Motor Vehicles) (3) and NHTSA (National Highway traffic safety administration) (4), 20% of accidents are related to drowsiness, being at the same level as accidents due to alcohol consumption with sometimes even worse consequences than those.
Solution
We will create a system that will be able to detect a person's drowsiness level, this with the aim of notifying the user about his state and if he is able to drive.
At the same time it will measure the driver’s attention or capacity to garner attention and if he is falling asleep while driving. If it positively detects that state (that he is getting drowsy), a powerful alarm will sound with the objective of waking the driver.
Additionally it will detect small vehicles and motorcycles in the automobile’s blind spots.
In turn, the system will have an accelerometer to generate a call to the emergency services if the car had an accident to be able to attend the emergency quickly.
Current Solutions:
Mercedes-Benz Attention Assist uses the car's engine control unit to monitor changes in steering and other driving habits and alerts the driver accordingly.
Lexus placed a camera in the dashboard that tracks the driver's face, rather than the vehicle's behavior, and alerts the driver if his or her movements seem to indicate sleep.
Volvo's Driver Alert Control is a lane-departure system that monitors and corrects the vehicle's position on the road, then alerts the driver if it detects any drifting between lanes.
Saab uses two cameras in the cockpit to monitor the driver's eye movement and alerts the driver with a text message in the dash, followed by a stern audio message if he or she still seems sleepy.
As you can see these are all premium brands and there is not a single plug and play system that can work for every car. This, is our opportunity as most cars in the road are not on that price range and do not have these systems.
Connection Diagram:
This is the connection diagram of the system:
Jetson Nano Setup:SD card Setup:
To download the operating system of the Jetson Nano enter the following link:
Format the SD card with SD Card Formatter and Flash the operating system in the SD with Etcher.
Hardware Setup:
Because we will be using several hardware components such as cameras, an accelerometer, speakers and more, I highly recommend a power supply of at least 5V - 4A.
The only hardware device that is not plug and play is the accelerometer because this one uses a I2C protocol (TWI if you speak Atmel). That's why we have to check if the Jetson Nano has these pins available.
Looks like it.
In this case we will use the Bus 1 because of its proximity to the 5V and GND pins. This will allow us to create a soldered circuit like an Arduino shield, but this time for the Jetson Nano.
Schematic of our "shield"
After soldering our circuit looks like so:
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This next video show us how to setup our hardware:
Curious Fact: The Nvidia Jetson Nano has the same IO pin distribution as a Raspberry Pi, so every shield for the Pi is backwards compatible with the Nano!
WiFi Setup:
The device has to be first set up with a WiFi connection, because of the fact that there are a ton of libraries we have to install. During the boot portion of the OS installation, we will connect the Jetson to your private WiFi and next we will set up the WiFi from our mobile phone (For this project as we need a Cellular connection). The advantage of doing this is that the Nano will connect to the one available so we can easily control this from the outside. TLDR: Connect both your private WiFi and your Mobile phone during the first setup, you the system can recognize both automatically.
Boot WiFi:
Home WiFi IP:
IP: 192.168.0.23
Mobile WiFi:
IP: 192.168.43.246
Software Setup:
We need to access our jetson from any command terminal via SSH, in the case of MAC and Linux just open the terminal and type the following command.
ssh youruser@yourip
The command that I use in MY case is:
ssh alex@192.168.0.23
In Window's case you can access it from any SSH connection assistant such as Putty.
The credentials for the Python code appear immediately upon entering, save the SID and the token, we will configure them later.
Verify your Personal Phone Number:
When you signed up for your trial account, you verified your personal phone number. You can see your list of verified phone numbers on the Verified Caller IDs page.
Go to your Verified Caller IDs page in the console.
Click on the red plus (+) icon to add a new number.
Enter the phone number you wish to receive the notification from Twilio.
Note: You will need access to this device to receive the call or text with your verification code.
Enter the verification code. You’re now ready to text or call this number with your trial Twilio account.
Get a Twilio Phone Number:
Go to the Phone Numbers page in the console.
Go to the Getting Started page in the console and click on "Get your first Twilio phone number".
A number will be automatically offered, save this number for later and press "Choose this number" to finish setting up your number.
Save the credentials of "Server", "User", "Password" and "Port".
Files Setup:
To edit the files we have to use the Vim editor that comes by default in the Jetson Nano, the following video shows what to do:
Setup boot start scripts:
In order for both files to run at the same time on the device's boot, we must run the following commands to create an rc.local file and give it the permission to run both scripts.
cd sudo mv "Jetson-Drowsiness-Driving-Monitor/Config File/rc.local" /etc/rc.local sudo chmod u+x /etc/rc.local
I grabbed a case and fit everything inside and this is how it looks:
These are the images it will show according to the CV detection, whether it sees a bike or a dog or anything else:
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If you want to add more images to the display, you have to use the following image2cpp converter, which will convert pixels into a code that Arduino IDE can use:
The web application gives us the code to copy and paste in our arduino project.
The device code is in the "Arduino" folder and all we have to do is configure the WiFi and MQTT credentials.
Drowsiness Monitor:
Let's go through a revision of the algorithms and procedures of both CV systems (Drowsiness and alert on one side and Blind spot detection on the other). The installation is remarkably easy as I have already provided an image for the project.
Please take a look at it for extensive explanation and documentation.
The sleep monitor uses the following libraries:
OpenCV:
Image processing
Haarcascades implementation.
Flashing speed detection.
Pygame:
Player sound alert.
Smbus:
Accelerometer reading.
Twilio:
Emergency notification delivery.
Requests:
Geolocation
The flicker detection algorithm is as follows:
Detection that there is a face of a person behind the wheel:
If a face is detected, perform eye search.
Thanks to the sensitivity of the Haarcascades we can use that in our favor. If a scaleFactor = 1.1 and minNeighbors = 40 is selected we can detect eyes only if the person has them open.
Because a blink lasts approximately 350 milliseconds then a single blink will not cause problems, however once the person keeps blinking for more than 2 or 3 seconds (according to our criteria) it will mean for the system that the person is falling asleep. Not separating the eyes from the road being one of the most important rules of driving.
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Also during the demo I found a very interesting use case, when one turns to look at his cell phone,the system also detects that you are not seeing the road. This being a new aspect that we will be exploring in future versions of the system to improve detection when a driver is not looking at the road and is distracted. But, for now it is one of those unintended great finds.
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Blind Spot Monitor:
The blind Spot monitor uses the following libraries:
OpenCV:
Image processing
DNN implementation.
The weights of the DNN were obtained from YoloV3.
MQTT:
Communication with the ESP32.
In this algorithm we use the detection of objects using YoloV3 and OpenCV Contrib which allows the use of DNN.
In this case, I created an algorithm to calculate the approximate distance at which an object is located using YOLOV3, the algorithm is as follows.
We calculate the aperture and distance of our camera, in my case it is 19 centimeters in aperture at a distance of 16 cm.
It means that an object 19 cm wide can be at least 16 cm away from the camera so that it can fully capture it, in this case the calculation of the distance of any object becomes a problem of similar triangles.
In this case, since our model creates boxes where it encloses our detected objects, we will use that data to obtain the apparent width of the object, in this case the approximate width will be stored in a variable during the calculation.
If we combine both terms we get the following equation.
In this case for the objects that we are going to detect, we approximate the following widths in centimeters.
{ person:70, car:220, motorcycle:120, dogs:50 }
Here is an example of its operation with the same image.
Testing the algorithm with the camera.
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Once having this distance, we will filter all distances that are greater than 2 meters, this being a safe distance to the car at the blind spot.
In turn, we will determine in which of the 2 blind spots the object is, right or left. Depending on the object and the side, the information will be sent via MQTT to the display. For example a car on the left side:
The Final Product:
Product:
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Notifications:
Epic DEMO:
Commentary:
I would consider the product finished as we only need a little of additional touches in the industrial engineering side of things for it to be a commercial product. Well and also a bit on the Electrical engineering perhaps to use only the components we need. That being said this functions as an upgrade from a project that a couple friends and myself are developing and It was ideal for me to use as a springboard and develop the idea much more. This one has the potential of becoming a commercially available option regarding Smart cities as the transition to autonomous or even smart vehicles will take a while in most cities.
That middle ground between the Analog, primarily mechanical-based private transports to a more "Smart" vehicle is a huge opportunity as the transition will take several years and most people are not able to afford it. Thank you for reading.
# -*- coding: utf-8 -*-"""@author: ALEX"""importnumpyasnpimportargparseimporttimeimportcv2importosimportpaho.mqtt.clientaspaho# This function trigger if the client connecteddefon_connect(client,userdata,flags,rc):print("Connection returned result: "+str(rc))#client.subscribe("#" , 1 ) # Wild Card# This function trigger every time we receive a message from the platformdefon_message(client,userdata,msg):print("topic: "+msg.topic)print("payload: "+str(msg.payload))# This function trigger when we publish defon_publish(client,obj,mid):print("mid: "+str(mid))# This function trigger when we subscribe to a new topic defon_subscribe(client,obj,mid,granted_qos):print("Subscribed: "+str(mid)+" "+str(granted_qos))# load the COCO class labels our YOLO model was trained onlabelsPath="yolo-coco/coco.names"LABELS=open(labelsPath).read().strip().split("\n")#objects=[70,200,120,50] #person:70, cars:220, motorcycle:120, dogs:50# Approximate width of the objects to calculate the distance in centimeters.objects=[70,220,120,50]#person:70, cars:220, motorcycle:120, dogs:50# initialize a list of colors to represent each possible class labelnp.random.seed(42)COLORS=np.random.randint(0,255,size=(len(LABELS),3),dtype="uint8")# derive the paths to the YOLO weights and model configurationweightsPath="yolo-coco/yolov3.weights"configPath="yolo-coco/yolov3.cfg"# load our YOLO object detector trained on COCO dataset (80 classes)print("[INFO] loading YOLO from disk...")net=cv2.dnn.readNetFromDarknet(configPath,weightsPath)# load our input image and grab its spatial dimensionscap=cv2.VideoCapture(0)cap.set(cv2.CAP_PROP_EXPOSURE,-10)# MQTT Startmqttc=paho.Client()mqttc.on_connect=on_connectmqttc.on_message=on_messagemqttc.on_publish=on_publishmqttc.on_subscribe=on_subscribemqttc.username_pw_set("USER","PASSWORD")mqttc.connect("URL",PORT,keepalive=60)rc=0while1:# Mqtt Client loopmqttc.loop()# Take Image From Cameraret,image=cap.read()# Saving the imagecv2.imwrite('image.jpg',image)# Open Again the imageimage=cv2.imread("image.jpg")# Obtaining the Height and Width (H,W)=image.shape[:2]# determine only the *output* layer names that we need from YOLOln=net.getLayerNames()ln=[ln[i[0]-1]foriinnet.getUnconnectedOutLayers()]# construct a blob from the input image and then perform a forward# pass of the YOLO object detector, giving us our bounding boxes and# associated probabilitiesblob=cv2.dnn.blobFromImage(image,1/255.0,(416,416),swapRB=True,crop=False)net.setInput(blob)start=time.time()layerOutputs=net.forward(ln)end=time.time()# show timing information on YOLOprint("[INFO] YOLO took {:.6f} seconds".format(end-start))# initialize our lists of detected bounding boxes, confidences, and# class IDs, respectivelyboxes=[]confidences=[]classIDs=[]# loop over each of the layer outputsforoutputinlayerOutputs:# loop over each of the detectionsfordetectioninoutput:# extract the class ID and confidence (i.e., probability) of# the current object detectionscores=detection[5:]classID=np.argmax(scores)confidence=scores[classID]# filter out weak predictions by ensuring the detected# probability is greater than the minimum probabilityifconfidence>0.5:# scale the bounding box coordinates back relative to the# size of the image, keeping in mind that YOLO actually# returns the center (x, y)-coordinates of the bounding# box followed by the boxes' width and heightbox=detection[0:4]*np.array([W,H,W,H])(centerX,centerY,width,height)=box.astype("int")# use the center (x, y)-coordinates to derive the top and# and left corner of the bounding boxx=int(centerX-(width/2))y=int(centerY-(height/2))# update our list of bounding box coordinates, confidences,# and class IDsboxes.append([x,y,int(width),int(height)])confidences.append(float(confidence))classIDs.append(classID)# apply non-maxima suppression to suppress weak, overlapping bounding# boxesidxs=cv2.dnn.NMSBoxes(boxes,confidences,0.5,0.3)## suppressing the labels that don't interest us distance=100000distancemem=100000labelmem=""labelmod=""pos=""# ensure at least one detection existsiflen(idxs)>0:# loop over the indexes we are keepingforiinidxs.flatten():# extract the bounding box coordinates(x,y)=(boxes[i][0],boxes[i][1])(w,h)=(boxes[i][2],boxes[i][3])# draw a bounding box rectangle and label on the imagecolor=[int(c)forcinCOLORS[classIDs[i]]]cv2.rectangle(image,(x,y),(x+w,y+h),color,2)height,width,channels=image.shape# Calculating on which side of the screen the object isif((width/2)<(x+(w/2))):pos="1"else:pos="0"# Checking if the label that the model sees is one of those that interest usif(LABELS[classIDs[i]]=="motorbike"):check=objects[2]labelmem="m"+poselif(LABELS[classIDs[i]]=="dog"):check=objects[3]labelmem="d"+poselif(LABELS[classIDs[i]]=="person"):check=objects[0]labelmem="p"+poselif(LABELS[classIDs[i]]=="car"):check=objects[1]labelmem="c"+poselse:check=1000# Calculating the distance with the algorithm explaining in Hacksterdistance=(check*16)/(19*(w/width))if(distance<distancemem):# Checking if the object is less than 3 meters from our car.if(300>distance):distancemem=distancelabelmod=labelmemprint(LABELS[classIDs[i]])text="{}: {}: {:.4f}".format(LABELS[classIDs[i]],str(distance),confidences[i])cv2.putText(image,text,(x,y-5),cv2.FONT_HERSHEY_SIMPLEX,0.5,color,2)# Sending the object that is nearby or a blank screenmqttc.publish('inTopic',labelmod)# Saving the processed imagecv2.imwrite('process.jpg',image)cap.release()cv2.destroyAllWindows()
Haarcascade Eye
XML
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Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistribution's of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistribution's in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * The name of Intel Corporation may not be used to endorse or promote products derived from this software without specific prior written permission. This software is provided by the copyright holders and contributors "as is" and any express or implied warranties, including, but not limited to, the implied warranties of merchantability and fitness for a particular purpose are disclaimed. 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This file has been truncated, please download it to see its full contents.
Haarcascade Face
XML
<?xml version="1.0"?><!-- Stump-based 24x24 discrete(?) adaboost frontal face detector. Created by Rainer Lienhart.//////////////////////////////////////////////////////////////////////////////////////// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. By downloading, copying, installing or using the software you agree to this license. If you do not agree to this license, do not download, install, copy or use the software. Intel License Agreement For Open Source Computer Vision Library Copyright (C) 2000, Intel Corporation, all rights reserved. Third party copyrights are property of their respective owners. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistribution's of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistribution's in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * The name of Intel Corporation may not be used to endorse or promote products derived from this software without specific prior written permission. This software is provided by the copyright holders and contributors "as is" and any express or implied warranties, including, but not limited to, the implied warranties of merchantability and fitness for a particular purpose are disclaimed. 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This file has been truncated, please download it to see its full contents.
coco.names
Typescript
person
bicycle
car
motorbike
aeroplane
bus
train
truck
boat
traffic light
fire hydrant
stop sign
parking meter
bench
bird
cat
dog
horse
sheep
cow
elephant
bear
zebra
giraffe
backpack
umbrella
handbag
tie
suitcase
frisbee
skis
snowboard
sports ball
kite
baseball bat
baseball glove
skateboard
surfboard
tennis racket
bottle
wine glass
cup
fork
knife
spoon
bowl
banana
apple
sandwich
orange
broccoli
carrot
hot dog
pizza
donut
cake
chair
sofa
pottedplant
bed
diningtable
toilet
tvmonitor
laptop
mouse
remote
keyboard
cell phone
microwave
oven
toaster
sink
refrigerator
book
clock
vase
scissors
teddy bear
hair drier
toothbrush
# -*- coding: utf-8 -*-"""@author: ALEX"""importsmbusimportnumpyasnpimporturllib.requestimportcv2importpygameimporttimeimportosimportmathimportrequestsfromtwilio.restimportClientimportrequestsimportjson# Setup the class of VMA accelerometer classMMA7455():bus=smbus.SMBus(1)def__init__(self):self.bus.write_byte_data(0x1D,0x16,0x55)# Setup the Modeself.bus.write_byte_data(0x1D,0x10,0)# Calibrateself.bus.write_byte_data(0x1D,0x11,0)# Calibrateself.bus.write_byte_data(0x1D,0x12,0)# Calibrateself.bus.write_byte_data(0x1D,0x13,0)# Calibrateself.bus.write_byte_data(0x1D,0x14,0)# Calibrateself.bus.write_byte_data(0x1D,0x15,0)# CalibratedefgetValueX(self):returnself.bus.read_byte_data(0x1D,0x06)defgetValueY(self):returnself.bus.read_byte_data(0x1D,0x07)defgetValueZ(self):returnself.bus.read_byte_data(0x1D,0x08)# Alarm sound filefile='alarm.mp3'# Sound player startpygame.init()pygame.mixer.init()# multiple cascades: https://github.com/Itseez/opencv/tree/master/data/haarcascade#https://github.com/Itseez/opencv/blob/master/data/haarcascades/haarcascade_frontalface_default.xmlface_cascade=cv2.CascadeClassifier('haarcascade/haarcascade_frontalface_default.xml')#https://github.com/Itseez/opencv/blob/master/data/haarcascades/haarcascade_eye.xmleye_cascade=cv2.CascadeClassifier('haarcascade/haarcascade_eye.xml')# Cam image input cap=cv2.VideoCapture(0)# Varsnf=1ne=1count=0# Accelerometer Declarationmma=MMA7455()# Obtaining the X, Y and Z values.xmem=mma.getValueX()ymem=mma.getValueY()zmem=mma.getValueZ()# Creating the base accelerometer values.if(xmem>127):xmem=xmem-255if(ymem>127):ymem=ymem-255if(zmem>127):zmem=zmem-255# Vars to get blinking frequencytime1=time.time()time2=time.time()# Crash Sensibilitysens=30# Sending SMS if Crash Detecteddefsend():# Your Account SID from twilio.com/consoleaccount_sid="Twilio SID"# Your Auth Token from twilio.com/consoleauth_token="Twilio Token"client=Client(account_sid,auth_token)phone="Registered Number"print('crash')send_url='http://ip-api.com/json'r=requests.get(send_url)j=json.loads(r.text)text="Alejandro Sanchez Crash Here: "text+="http://maps.google.com/maps?q=loc:{},{}".format(j['lat'],j['lon'])print(text)message=client.messages.create(to=phone,from_="Twilio Number",body=text)print(message.sid)time.sleep(10)stop()# Script Looptry:while1:# Check if we have a crash with the acceleration samplesx=mma.getValueX()y=mma.getValueY()z=mma.getValueZ()if(x>127):x=x-255if(y>127):y=y-255if(z>127):z=z-255# Send sms if crashif(abs(xmem-x)>sens):send()if(abs(ymem-y)>sens):send()if(abs(zmem-z)>sens):send()# Take Image From Cameraret,img=cap.read()# Gray scale convertion gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)# Obtaining the faces in the imagefaces=face_cascade.detectMultiScale(gray,1.3,5)for(x,y,w,h)infaces:cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)roi_gray=gray[y:y+h,x:x+w]roi_color=img[y:y+h,x:x+w]# If there is a face check that we can detect the eyeseyes=eye_cascade.detectMultiScale(roi_gray,1.3,40)ne=len(eyes)for(ex,ey,ew,eh)ineyes:cv2.rectangle(roi_color,(ex,ey),(ex+ew,ey+eh),(0,255,0),2)# Con esta configuracin si cerramos los ojos el sistema no detecta los ojosnf=len(faces)# If we can see a face but cannot detect its eyes then start a counter.if(nf>0andne<1):time1=time.time()print(time1-time2)# If we cannot detect the eyes for more than 2 seconds the alarm will sound.if((time1-time2)>=2):pygame.mixer.music.load(file)pygame.mixer.music.play()else:# If we detect the eyes again, we stop the alarm and reset the counters.pygame.mixer.music.stop()time1=time.time()time2=time1# "Stop" FunctionexceptNameError:print("Crash Exception")
_mjDzLRdAv9.gif?auto=format%2Ccompress&gifq=35&w=400&h=300&fit=min)
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