AI for Smart Policing (For The State Of Telangana)
Hello reader welcome here, firstly i would like to introduce you the basic term of this article then i want to tell you the core ideas of artificial intelligence which is very benifitial for the smart policing of our nation.
What Is AI (Artificial Intelligence)
Artificial intelligence ,if i explain in simple word’s then artificial intelligence is a technology or process in which human trained the machine for perform some specific task through the using that task related machine learning model.
Example
Case1. we have a lot of case of AI in our real world which we can see around us , when you click a photo through your android phone and if you ever notice then it show your age by detecting your face and sometime when you enable a AI mode in your phone then it show the name of the objects.
Case2. when we talk to our google assistant then it gives reply like a human it is also a part of the artificial intelligence.
Terminology
Artificial Intelligence refers to the phenomenon where a machine acts as a blueprint of the human mind, by being able to understand, analyze, and learn from data through specially designed algorithms. Artificially intelligent machines can remember human behavior patterns and adapt according to their preferences.
Structure Of the Artificial Intelligence
Machine Learning
Machine learning algorithms identify patterns and/or predict outcomes. Many organizations sit on huge data sets related to customers, business operations, or financials. Human analysts have limited time and brainpower to process and analyze this data. Therefore, machine learning can be used to:
- Predict outcomes given input data, like regression analysis but on much larger scales and with multiple variables. A perfect example is algorithmic trading, where the trading model must analyze vast amounts of input data and recommend profitable trades. As the model keeps working with real-world data, it can even ‘improve’ itself and adapt its trading strategies to market conditions.
- Find insights or patterns in large data sets that human eyes sometimes miss. For example, a company can study how its customer purchase patterns are evolving and use the findings to modify their product lines.
- Do a lot more in less time. Goodbye grunt work.
Many AI methodologies including neural networks, deep learning, and evolutionary algorithms, are related to machine learning.
Machine Learning in action: Netflix
What you see on Netflix is tailored to you and people with similar preferences to you
Netflix says they “invest heavily in machine learning to continually improve our member experience and optimize the Netflix service end-to-end.”
Netflix applies machine learning to your viewing history to personalize the movie TV show recommendations you see. Netflix also analyzes what you and people with similar preferences watched in the past, and even auto generates personalized thumbnails and artwork for movie titles, to entice you to click on a title that you’d otherwise ignore.
All to ensure that you stay glued to the screen while your brain melts.
Neural Networks and Deep Learning
A neural network tries to replicate the human brain’s approach to analyzing data. They can identify, classify and analyze diverse data, deal with many variables, and find patterns that are too complex for human brains to see.
Deep learning is a subset of machine learning. When applied to a neural network, it allows the network to learn without human supervision from unstructured data (data that isn’t classified or labeled). This is perfect for analyzing ‘big data’ that organizations collect. These big data sets include different data formats such as text, images, video and sound.
Neural networks are frequently combined with machine learning, deep learning, and computer vision (training computers to derive meaning from pictures). That’s why people talk about ‘deep neural networks,’ which is basically a neural network with more than 2 layers. More layers = more analytical power.
Deep neural networks can be trained to identify and classify objects. A cool use is facial recognition — identifying unique faces in photos and videos. Neural networks also learn over time. For instance, they get better at classifying objects and identifying faces as they are fed more data.
Neural networks and deep learning in action: Facial recognition in China
China is doing a lot with facial recognition. Which makes sense, because there are cameras everywhere in China. Many cameras mean plenty of data for deep neural networks to use. In the interest of time, here are three examples.
At University…
A university in Eastern China has implemented an AI-powered attendance system, with cameras that constantly observe students in class.
Naturally, it scans faces to check that the student actually turns up to class. More importantly, it also analyzes facial expressions in real time, and can judge whether students are paying attention. It can apparently recognize people are sleeping or playing on their phones. I’ll bet you’re thinking ‘I’m glad they didn’t have this tech at my university…’
For Payments…
While people are still talking about mobile payments using WeChat Pay and Alipay, these companies are already moving on to the next phase: face-based payments. Why bother with your phone when you can pay by looking into a camera?
Alipay’s Dragonfly facial recognition system has expanded to over 300 cities in China. WeChat Pay also has a similar system. Businesses from bakeries to supermarkets have adopted these systems to speed up customer payments. After a face is scanned, money is deducted from the customer’s Alipay or WeChat Pay account. Businesses benefit from spending less on cashier staff.
Face-based payments also benefit less tech-savvy citizens such as elderly people. Also, only 60% of China’s 1.4bn population is connected to the internet and only 40% of the population pays with smartphones. In future, we can even expect these unconnected citizens to use face-based payments if their face can be linked to a bank account.
To Fight Crime…
AI firms in China are using facial recognition to help law enforcement track suspects.
State-backed Cloudwalk’s facial recognition tech has helped police make over 10,000 arrests and is used in 29 Chinese provinces. It makes over 1 billion facial comparisons daily and has amassed over 100 billion data points.
SenseTime made the news recently for using facial recognition to identify one suspect in a crowd of 50,000 concertgoers in China.
Watrix, another computer vision firm, has apparently decided that facial recognition is old news. Their tech helps police track people by the way they walk and move, even if their face is hidden. Also, you can’t fool their tech by adding a limp or altering your gait.
Now We Will Discuss Here Some Smart Policing Technique Which Is Done With The Help Of The Artificial intelligence.
AI Video Surveillance Platform
In a first for India, Uttar Pradesh has deployed AI-enabled video analytics platform ‘Jarvis’ in 70 prisons to monitor inmates States like UP, Rajasthan and Punjab face huge challenge of increasing crime rates making it imperative for state police department and CM office to use technology like AI + IoT and Video technology for real time alerts, crime control and any threats to security of inmates in prisons across states.
- Time to identify criminals and time to justice for crimes is lowest amongst developing countries hence since 2015 we saw creation of AI based Video analytics solutions.
- AI Video Warden for Prisons in Uttar Pradesh (UP)
- Staqu accessed ~1 million historic violence videos from UP Police on positioning of CCTV cameras. Following are some of the data points studied to build machine learning algorithm within Jarvis which is a ready to use platform from Staqu.
Smart RoboCop For Police Department
In December 2017, the Telangana Police launched a Smart RoboCop equipped with cameras, GPS, and sensors including ultrasonic readers, proximity sensors, and temperature sensors. They were developed by H-Bots Robotics, a Hyderabad-based company, with the help of AI. The robot is designed to assist the police in managing law and order, and traffic management. The robot can take care of security at selected spots such as malls, airports, and other public places. It can also recognize people, take complaints, detect bombs, identify suspects, interact with people, and answer people’s queries. The robot has inbuilt technology such as thermal imaging, emergency flashlights and has an automatic charging dock station.
Predictive Policing Software
In 2015, the Government of Maharashtra introduced a ₹800 crore plan (consisting of five major projects) to secure the cyberspace in the state. The initiative included the development of a software which would help the police department in preventing crimes at specific place and time. The software is based on an algorithm which uses AI and big data analytics to create an exhaustive database of crime. The algorithm will use available data with police and open source information on the internet. The software will help the police in obtaining points for location, type of event and probable gang before any incident.
The government is also planning to introduce a predictive policing scheme which will use past precedents of miscreants through data mining and high-end tools with the help of a data centre. In March 2018, PwC was appointed as consultant for the project and ₹650 crore was allocated for capacity building and other expenditures.
AI-Powered Mobile Application For Road-Related Public Complaints
In October 2018, the Government of Haryana and CivilCops signed a deal to develop an AI solution for analyzing road-related public complaints. CivilCops is a Delhi-based social intelligence startup which leverages data analytics and artificial intelligence to make the process of reporting grievances faster through chat and voice interfaces. The company will develop an application to replace the Harpath app (Haryana Government application which offers solutions for road-related grievances in the state).
The new application will use AI to automate the process of receiving and analyzing complaints from the citizens using chat and voice interfaces, thus reducing manpower and cost involved in the process. CivilCops will integrate the chat interface on Facebook Messenger platform which will allow the citizens to search for a department or scan a QR code that will generate an automated messaging popup
After The Above Study Case Now Thought which i have emerge in my mind which is Given Below
AI For The Smart Traffic Policing Light
if we talk about the traffic light in cities then it is very useful for the daily routine of transport purpose but did you ever think that traffic light is not benifitial every time for us.
Why it’s not benifitial for us ??
because in the traffic light system normally timer is set for the particular for the four side of the road which change one by one after time interval completed it’s doesn’t matter which road side have huge traffic or which road side have less or which side of the road traffic is not available so for enhance this portion of traffic light i have one idea for that.
Enhancement of the traffic light
With the help of Artificial intelligence we can set multiple machine learning model in traffic light so if any side of the road have no any kind of traffic then we don’t switch the time counter for that side because that side is clear we don’t have to switch their because if we switch their then we waste the time of the people of the other three side.
Process
STEP1:- (MACHINE LEARNING MODEL)
for implementing this technique this in traffic light system first we have to train machine learning model with good accuracy, accuracy should be near about 90 to 95 % then it will work very fine otherwise it will generate problem.
STEP2:-(COMPUTER VISION CV2)
After train this machine learning model then we have to use computer vision for connect to this model to the cctv cameras.
and we have to set these cctv cameras four side on the traffic system, the quality of these cctv camera should be also good.
CONCLUSION
As we all know that time is very benifitial for us so for save the time i think it is a very fine approach because in normal day life we have to spent more time in traffic its due to some lack of our traffic system,
………………………. Thankyou…………………………………………
