IOT Cameras to Catch Drunk Driving
The Scenario
- Drunk Driving is Extremely Difficult to catch
- Lots of cameras for traffic and speeding
- Not used currently for drunk driving
- Police look find drunk drivers by sending officers to look for them
- Can take advantage of machine learning and data science
The Scenario
Camera
Camera
Camera
Camera
Cloud/Fog/Server/
Load Balancer
Desired Outcome
- Fully Automated stream of data
- Reduce costs for governments (less police searching)
- Improved accuracy/higher rate of success for catching possible drunk/reckless driving
Implementation
- COAP Protocol as the primary protocol
- Similar to HTTP
- Can add DTLS for security
- Optimal for sending video
- Needed an application level protocol
Implementation - Continued
- Restrictions on Data Set
- Couldn't find good dataset for traffic cameras
- Need to collect from a continuous feed of publicly accessible traffic cameras.
- For this demo, from dashboard perspective
- Possible scenario if we have cameras on cars (not likely given privacy laws
- Couldn't find good dataset for traffic cameras
- https://registry.opendata.aws/ford-multi-av-seasonal/
Implementation - Continued
- Client - Cameras
- Sends all images to server
- Currently is command line; however, ideally this will be autonomous.
async def send_content(self, foldername):
# gets the folder of images to send files
folder = os.path.dirname(foldername)+foldername
logging.log(logging.INFO, "Folder to send: " + folder)
# Loop throuhg all files, and send the data.
for f in os.listdir(folder):
fullpath = os.path.join(folder, f)
context = await Context.create_client_context()
with open (fullpath, "rb") as img:
self.image = img.read()
request = Message(code=POST, payload=self.image, uri="coap://localhost/api/1.0/receiveVideo")
response = await context.request(request).response
print("Result: %s\n%r" % (response.code, response.payload))
Implementation - Continued
- Server - Cloud, Fog, etc
- Reads all of the files it receives
- Stores them
- Later it will be used for processing by ML/data science algorithms.
async def render_post(self, request):
global imageType
# Gets the current time and puts it in a specific format
currentTime = datetime.datetime.now().strftime("-%Y-%m-%d-%H-%M-%S")
logging.log(logging.INFO, "Current Time: " + currentTime)
#makes directory if it doesn't exist (for receiving files)
if not os.path.exists('receivedFiles'):
os.makedirs('receivedFiles')
logging.log(logging.INFO, "Created new folder for receiving files")
# Constructs the file path and writes the file that it has received
filename = "./receivedFiles/trafficVideo" + currentTime + ".png"
logging.log(logging.INFO, "FileName: " + filename)
logging.log(logging.DEBUG, str(request.payload))
with open(filename, "wb") as f:
try:
f.write(request.payload)
logging.log(logging.INFO, "Successfully wrote to file")
except e:
logging.log(logging.ERROR, e)
# constructs and sends response message
return aiocoap.Message(code=aiocoap.CREATED, payload="File received".encode("utf-8"))
Demo
Improvements
- Server
- Object Detection (to detect driving behavior)
- Requires use of Deep Neural Network
- Camera
- Save bandwidth using MQTT to start/stop recording
- Select few cameras will always be recording
- Cameras don't necessarily have to be record and send data all the time
- May require basic machine learning
- Save bandwidth using MQTT to start/stop recording
Improvements - Continued
IOT Scenario - Cameras Detecting Drunk Driving
By Ragnar Security
IOT Scenario - Cameras Detecting Drunk Driving
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