Vehicle monitoring system
This was my project for the 4rth year cloud computing subject. The task was to do a project that can be used to increase the security of my university by using a cloud-based service.
The security of a university depends on the vehicles enters the campus premises. Number plate recognition can be used to confirm the entering vehicle’s identity for security purposes. In most of the universities, this is done using manually. But with the help of VNPR system vehicles can be recognized.
Basically what the system does is monitoring vehicles entering or leaving the university premises.
When a vehicle comes near to the gate it captures an image of the vehicle then detect its plate number, model make and checks with the database for its data to check whether that vehicle is allowed in that area etc. So as the cloud-based service we used an API provided by open ALPR for vehicle detection.
After recognizing the number plate, the system can send notification about the timing of vehicle entering or exit. Using this system mentioned.
The OpenALPR Cloud API is a web-based service that analyzes images for license plates as well as vehicle information such as make, model, and colour. The Cloud API service is easy to integrate into the application via a web-based REST service. When we send image data to the OpenALPR API, it process that data and return JSON data describing the license plate and vehicle.
The security of a university depends on the vehicles enters the campus premises. Number plate recognition can be used to confirm the entering vehicle’s identity for security purposes. In most of the universities, this is done using manually. But with the help of VNPR system vehicles can be recognized.
Basically what the system does is monitoring vehicles entering or leaving the university premises.
When a vehicle comes near to the gate it captures an image of the vehicle then detect its plate number, model make and checks with the database for its data to check whether that vehicle is allowed in that area etc. So as the cloud-based service we used an API provided by open ALPR for vehicle detection.
After recognizing the number plate, the system can send notification about the timing of vehicle entering or exit. Using this system mentioned.
The OpenALPR Cloud API is a web-based service that analyzes images for license plates as well as vehicle information such as make, model, and colour. The Cloud API service is easy to integrate into the application via a web-based REST service. When we send image data to the OpenALPR API, it process that data and return JSON data describing the license plate and vehicle.
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| Basic block diagram of the system |
Hardware used:-
Since this is a Prototype we use Rasberry PI to and a Pi Cam. The Pi cam captures the image of a vehicle number plate and then it processes through the Rasberry board.
Below is an example of a JSON response from open ALPR:
{
"uuid" : "",
"regions_of_interest" : [
{
"height" : 600,
"width" : 600,
"y" : 0,
"x" : 0
}
],
"credits_monthly_used" : 7040,
"credit_cost" : 2,
"img_height" : 600,
"error" : false,
"epoch_time" : 1522978197756,
"version" : 2,
"results" : [
{
"plate_index" : 0,
"vehicle_region" : {
"y" : 7,
"width" : 568,
"height" : 568,
"x" : 16
},
"processing_time_ms" : 68.9315719604492,
"vehicle" : {
"body_type" : [
{
"name" : "sedan-compact",
"confidence" : 89.6389389038086
},
{
"name" : "suv-standard",
"confidence" : 2.92187452316284
},
{
"name" : "sedan-wagon",
"confidence" : 2.83985614776611
}
],
"year" : [
{
"confidence" : 47.3032341003418,
"name" : "2000-2004"
},
{
"name" : "2005-2009",
"confidence" : 39.6665573120117
},
{
"name" : "1995-1999",
"confidence" : 7.91491031646729
}
],
"make_model" : [
{
"confidence" : 40.9104766845703,
"name" : "chevrolet_hhr"
},
{
"name" : "toyota_ist",
"confidence" : 22.9741859436035
},
{
"confidence" : 6.41662836074829,
"name" : "chevrolet_trailblazer"
},
{
"confidence" : 1.58923923969269,
"name" : "mazda_verisa"
},
{
"confidence" : 1.31779313087463,
"name" : "nissan_micra"
},
{
"name" : "mazda_tribute",
"confidence" : 1.24455153942108
},
{
"confidence" : 0.991917014122009,
"name" : "chevrolet_uplander"
}
],
"make" : [
{
"confidence" : 32.4275550842285,
"name" : "chevrolet"
},
{
"name" : "toyota",
"confidence" : 31.9965953826904
},
{
"confidence" : 15.4623928070068,
"name" : "nissan"
},
{
"confidence" : 8.25705337524414,
"name" : "daihatsu"
},
{
"name" : "mazda",
"confidence" : 3.8371422290802
}
],
"color" : [
{
"name" : "silver-gray",
"confidence" : 73.2146682739258
},
{
"name" : "blue",
"confidence" : 15.9568424224854
}
],
"orientation" : [
{
"name" : "180",
"confidence" : 97.7202453613281
},
{
"confidence" : 1.84529066085815,
"name" : "225"
}
]
},
"matches_template" : 1,
"plate" : "627WWI",
"requested_topn" : 10,
"coordinates" : [
{
"x" : 237,
"y" : 357
},
{
"y" : 359,
"x" : 364
},
{
"x" : 362,
"y" : 416
},
{
"x" : 237,
"y" : 414
}
],
"region_confidence" : 99,
"region" : "wa",
"candidates" : [
{
"matches_template" : 1,
"confidence" : 94.9990844726562,
"plate" : "627WWI"
}
],
"confidence" : 94.9990844726562
}
],
"credits_monthly_total" : 10000000000,
"img_width" : 600,
"data_type" : "alpr_results",
"processing_time" : {
"total" : 621.703000000025,
"plates" : 140.277725219727,
"vehicles" : 476.741000000004
}
}
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