Our Main Projects:
Intelligent Traffic and vehicle Number plate Recognition system.
Monitor traffic and automate smart traffic signals and Suspicious activity with simple database searches that reveal the full history of any vehicle that drove past a camera or easily detect and recognize Number plates from surveillance video.
Our aim is to reduce the manual interface to minimal. Smart traffic signals, artificial intelligence to determine the flow of traffic, automated enforcement and communication to change the face of the traffic situation in Karachi
We provide the module of vehicle and Number plate detection based on deep learning is extremely effective for roads with heavy traffic flow, hospitals, educational institutes, parks and malls where managing traffic is difficult. By applying this method, users can achieve data related the number of vehicles parked and the space available. The user will get information of the number and types of vehicles on the screen by deploying cameras on the field. Number plate identification is also being incorporated for identifying certain cars when there are in a particular vicinity within the city.
Ideally, a traffic official on the road would leave the carriageway opened for equal minutes for smoother flow of traffic. However, not all carriageways have similar volume of traffic which means that the carriageways should be opened for a particular time duration depending on the volume of traffic it has. AI would use camera live feeds, sensors and even Google Maps to make a predictive algorithm and instruct automated traffic signals to work accordingly
Face recognition software
Face recognition is a method of identifying or verifying the identity of an individual using their face. Face recognition systems can be used to identify people in photos, video, or in real-time. Law enforcement may also use mobile devices to identify people during police stops. But face recognition data can be prone to error, which can implicate people for crimes they haven’t committed. Facial recognition software is particularly bad at recognizing African Americans and other ethnic minorities, women, and young people, often misidentifying or failing to identify them, disparately impacting certain groups. Additionally, face recognition has been used to target people engaging in protected speech. In the near future, face recognition technology will likely become more ubiquitous. It may be used to track individuals’ movements out in the world like automated license plate readers track vehicles by plate numbers. Real-time face recognition is already being used in other countries and even at sporting events in the United States. How Face Recognition Works Source: Iowa Department of Transportation Face recognition systems use computer algorithms to pick out specific, distinctive details about a person’s face. These details, such as distance between the eyes or shape of the chin, are then converted into a mathematical representation and compared to data on other faces collected in a face recognition database. The data about a particular face is often called a face template and is distinct from a photograph because it’s designed to only include certain details that can be used to distinguish one face from another. Some face recognition systems, instead of positively identifying an unknown person, are designed to calculate a probability match score between the unknown person and specific face templates stored in the database. These systems will offer up several potential matches, ranked in order of likelihood of correct identification, instead of just returning a single result. Face recognition systems vary in their ability to identify people under challenging conditions such as poor lighting, low quality image resolution, and suboptimal angle of view (such as in a photograph taken from above looking down on an unknown person). When it comes to errors, there are two key concepts to understand: A “false negative” is when the face recognition system fails to match a person’s face to an image that is, in fact, contained in a database. In other words, the system will erroneously return zero results in response to a query. A “false positive” is when the face recognition system does match a person’s face to an image in a database, but that match is actually incorrect. This is when a police officer submits an image of “Joe,” but the system erroneously tells the officer that the photo is of “Jack.” When researching a face recognition system, it is important to look closely at the “false positive” rate and the “false negative” rate, since there is almost always a trade-off. For example, if you are using face recognition to unlock your phone, it is better if the system fails to identify you a few times (false negative) than it is for the system to misidentify other people as you and lets those people unlock your phone (false positive). If the result of a misidentification is that an innocent person goes to jail (like a misidentification in a mugshot database), then the system should be designed to have as few false positives as possible. How Law Enforcement Uses Face Recognition Source: Arizona Department of Transportation Law enforcement agencies are using face recognition more and more frequently in routine policing. Police collect mugshots from arrestees and compare them against local, state, and federal face recognition databases. Once an arrestee’s photo has been taken, the mugshot will live on in one or more databases to be scanned every time the police do another criminal search. Law enforcement can then query these vast mugshot databases to identify people in photos taken from social media, CCTV, traffic cameras, or even photographs they’ve taken themselves in the field. Faces may also be compared in real-time against “hot lists” of people suspected of illegal activity. Mobile face recognition allows officers to use smartphones, tablets or other portable devices to take a photo of a driver or pedestrian in the field and immediately compare that photo against one or more face recognition databases to attempt an identification. In San Diego, for example, a program called TACIDS (Tactical Identification System) allows law enforcement officers from nearly 25 agencies to stop people on the street, use their tablets or mobile phones to take photographs of them and run the images against the county’s mugshot database. Face recognition has been used in airports, at border crossings, and during events such as the Olympic Games. Face recognition may also be used in private spaces like stores and sports stadiums, but different rules may apply to private sector face recognition. Supporting these uses of face reconition are scores of databases at the local, state and federal level. Estimates indicate that 25% or more of all state and local law enforcement agencies in the U.S. can run face recognition searches on their own databases or those of another agency. According to Governing magazine, as of 2015, at least 39 states used face recogntion software with their Department of Motor Vehicles (DMV) databases to detect fraud. The Washington Post reported in 2013 that 26 of these states allow law enforcement to search or request searches of driver license databases, however it is likely this number has increased over time. Databases are also found at the local level, and these databases can be very large. For example, the Pinellas County Sheriff’s Office in Florida may have one of the largest local face analysis databases. According to research from Georgetown University, the database is searched about 8,000 times a month by more than 240 agencies. The federal government has several face recognition systems, but the database most relevant for law enforcement is FBI’s Next Generation Identification database which contains more than 30-million face recognition records. FBI allows state and local agencies “lights out” access to this database, which means no human at the federal level checks up on the individual searches. In turn, states allow FBI access to their own criminal face recognition databases. FBI also has a team of employees dedicated just to face recognition searches called Facial Analysis, Comparison and Evaluation (“FACE”) Services. The FBI can access over 400-million non-criminal photos from state DMVs and the State Department, and 16 U.S. states allow FACE access to driver’s license and ID photos. Given the large number of DMV databases using face recognition and the number of Americans whose photos are in the State Department’s database of passport and U.S. visa holders, Georgetown University has estimated close to half of all American adults have been entered into at least one if not more face recognition databases.