Bournemouth Borough Council is taking the first steps towards a fixed deterrent to reduce the increasing incidents of inconsiderate parking outside schools by deploying Videalert’s fully hosted CCTV-based enforcement platform. Six cameras will be deployed as part of a year-long pilot scheme at an initial three school locations where illegal parking on the yellow ‘keep clears’ has been identified as putting children’s lives in danger.

CCTV solution features

According to Elaine Smith, Deputy Parking Services Manager at Bournemouth Borough Council: “The zig-zag lines provided outside our schools are there to protect our children, making the road safer to cross. We have in the past tried using camera cars, but it is hoped that using fixed CCTV cameras will prove to be more effective in changing the anti-social behaviour of those who drop off and pick up their children in restricted areas.”

Videalert’s CEaaS solution is easy to install, as it does not require the procurement and deployment of any equipment within Bournemouth’s IT infrastructure or software to be installed on local PCs for evidence pack review. A single PTZ camera installed on a lamp post continuously monitors the keep clear zones and automatically captures only the drivers that are stationary in defined ‘watch areas’ and exceed the ‘watch times’. The system combines automatic number plate recognition (ANPR) with video analytics to deliver the highest productivity at the lowest operational cost.

"This innovative solution also allows for other civil traffic enforcement requirements to be easily and rapidly added"

CCTV benefits for traffic management

Qualified council operatives can access and review evidence packs using standard web browsers before transmitting confirmed offences to the back office processing system where PCNs are issued. Videalert is providing Bournemouth Borough Council with a dedicated virtual server to guarantee the highest level of security and integrity with cloud storage used for backup.

“Outsourcing to a hosted enforcement environment enables councils to improve efficiency, maximise productivity and maintain greater control over costs,” added Tim Daniels, Sales and Marketing Director of Videalert. “This innovative solution also allows for other civil traffic enforcement requirements, such as bus lanes, to be easily and rapidly added; as well as providing a platform for delivering traffic management and community safety applications simultaneously without needing a major IT infrastructure project.”

Download PDF version

In case you missed it

What is the value of remote monitoring systems’ health and operation?
What is the value of remote monitoring systems’ health and operation?

When is it too late to learn that a video camera isn’t working properly? As any security professional will tell you, it’s too late when you find that the system has failed to capture critical video. And yet, for many years, system administrators “didn’t know what they didn’t know.” And when they found out, it was too late, and the system failed to perform as intended. Fortunately, in today’s technology-driven networked environment, monitoring a system’s health is much easier, and a variety of systems can be deployed to ensure the integrity of a system’s operation. We asked this week’s Expert Panel Roundtable: How can remote monitoring of a security system’s health and operation impact integrators and end users?

What is the changing role of training in the security industry?
What is the changing role of training in the security industry?

Even the most advanced and sophisticated security systems are limited in their effectiveness by a factor that is common to all systems – the human factor. How effectively integrators install systems and how productively users interface with their systems both depend largely on how well individual people are trained. We asked this week’s Expert Panel Roundtable: What is the changing role of training in the security and video surveillance market?

What is AI Face Search? Benefits over facial recognition systems
What is AI Face Search? Benefits over facial recognition systems

When a child goes missing in a large, crowded mall, we have a panicking mom asking for help from the staff, at least a dozen cameras in the area, and assuming the child has gone missing for only 15 minutes, about 3 hours’ worth of video to look through to find the child. Typical security staff response would be to monitor the video wall while reviewing the footage and making a verbal announcement throughout the mall so the staff can keep an eye out for her. There is no telling how long it will take, while every second feels like hours under pressure. As more time passes, the possible areas where the child can be will widen, it becomes more time-consuming to search manually, and the likelihood of finding the child decreases. What if we can avoid all of that and directly search for that particular girl in less than 1 second? Artificial neural networks are improving every day and now enable us to search for a person across all selected camera streamsWith Artificial Intelligence, we can. Artificial neural networks are improving every day and now enable us to search for a person across all selected camera streams in a fraction of a second, using only one photo of that person. The photo does not even have to be a full frontal, passport-type mugshot; it can be a selfie image of the person at a party, as long as the face is there, the AI can find her and match her face with the hundreds or thousands of faces in the locations of interest. The search result is obtained in nearly real time as she passes by a certain camera. Distinguishing humans from animals and statues The AI system continuously analyses video streams from the surveillance cameras in its network, distinguishes human faces from non-human objects such as statues and animals, and much like a human brain, stores information about those faces in its memory, a mental image of the facial features so to speak. When we, the system user, upload an image of the person of interest to the AI system, the AI detects the face(s) in that image along with their particular features, search its memory for similar faces, and shows us where and when the person has appeared. We are in control of selecting the time period (up to days) and place (cameras) to search, and we can adjust the similarity level, i.e., how much a face matches the uploaded photo, to expand or fine-tune the search result according to our need. Furthermore, because the camera names and time stamps are available, the system can be linked with maps to track and predict the path of the person of interest. AI Face Search is not Face Recognition for two reasons: it protects people’s privacy, and it is lightweight Protecting people’s privacy with AI Face Search  All features of face recognition can be enabled by the system user, such as to notify staff members when a person of interest is approaching the store AI Face Search is not Face Recognition for two reasons: it protects people’s privacy, and it is lightweight. First, with AI Face Search, no names, ID, personal information, or lists of any type are required to be saved in the system. The uploaded image can be erased from the system after use, there is no face database, and all faces in the camera live view can be blurred out post-processing to guarantee GDPR compliance. Second, the lack of a required face database, a live view with frames drawn around the detected faces and constant face matching in the background also significantly reduces the amount of computing resource to process the video stream, hence the lightweight. Face Search versus Face Recognition AI Face Search Face Recognition Quick search for a particular person in video footage Identify everyone in video footage Match detected face(s) in video stream to target face(s) in an uploaded image Match detected face(s) in video stream to a database Do not store faces and names in a database Must have a database with ID info Automatically protect privacy for GDPR compliance in public places May require additional paperwork to comply with privacy regulations Lightweight solution Complex solution for large-scale deployment Main use: locate persons of interest in a large area Main use: identify a person who passes through a checkpoint Of course, all features of face recognition can be enabled by the system user if necessary, such as to notify staff members when a person of interest is approaching the store, but the flexibility to not have such features and to use the search tool as a simple Google-like device particularly for people and images is the advantage of AI Face Search.Because Face Search is not based on face recognition, no faces and name identifications are stored Advantages of AI Face Search Artificial Intelligence has advanced so far in the past few years that its facial understanding capability is equivalent to that of a human. The AI will recognise the person of interest whether he has glasses, wears a hat, is drinking water, or is at an angle away from the camera. In summary, the advantages of Face Search: High efficiency: a target person can be located within a few seconds, which enables fast response time. High performance: high accuracy in a large database and stable performance, much like Google search for text-based queries. Easy setup and usage: AI appliance with the built-in face search engine can be customised to integrate to any existing NVR/VMS/camera system or as a standalone unit depending on the customer’s needs. The simple-to-use interface requires minimal training and no special programming skills. High-cost saving: the time saving and ease of use translate to orders of magnitude less manual effort than traditionally required, which means money saving. Scalability: AI can scale much faster and at a wider scope than human effort. AI performance simply relies on computing resource, and each Face Search appliance typically comes with the optimal hardware for any system size depending on the customer need, which can go up to thousands of cameras. Privacy: AI Face Search is not face recognition. For face recognition, there are privacy laws that limits the usage. Because Face Search is not based on face recognition, no faces and name identifications are stored, so Face Search can be used in many public environments to identify faces against past and real-time video recordings. AI Face Search match detected face(s) in video stream to target face(s) in an uploaded image Common use cases of AI Face Search In addition to the scenario of missing child in a shopping mall, other common use cases for the AI Face Search technology include: Retail management: Search, detect and locate VIP guests in hotels, shopping centres, resorts, etc. to promptly attend to their needs, track their behaviour pattern, and predict locations that they tend to visit. Crime suspect: Quickly search for and prove/disprove the presence of suspects (thief, robber, terrorist, etc.) in an incident at certain locations and time. School campus protection: With the recent increase in number of mass shootings in school campuses, there is a need to identify, locate and stop a weapon carrier on campus as soon as possible before he can start shooting. Face Search will enable the authorities to locate the suspect and trace his movements within seconds using multiple camera feeds from different areas on campus. Only one clear image of the suspect’s face is sufficient. In the race of technology development in response to business needs and security concerns, AI Face Search is a simple, lightweight solution for airports, shopping centres, schools, resorts, etc. to increase our efficiency, minimise manual effort in searching for people when incidents occur on site, and actively prevent potential incidents from occurring. By Paul Sun, CEO of IronYun, and Mai Truong, Marketing Manager of IronYun