Ikegami CCTV Cameras

In 1901 New York state made a pioneering regulation move and became the first US state to require automobile owners to register their vehicles. This marked the beginning of regulation on modern traffic, which - following decades of development - resulted in a multi-layer concept of regulation relating to vehicles and driver’s licenses, traffic signs and insurance mechanisms that we are all familiar with nowadays. While certain parallels can be drawn between the early days of cars and our contemporary experience with quadcopters, we are facing a new challenging era that is far more complex to organise and regulate. Integrating drones in existing regulatory ecosystem Similar to other pioneering technologies in the past, drones need to integrate into a long existing and well-balanced ecosystem, the rules of which have first been drafted some one hundred years ago and have evolved without taking vehicles such as drones into account. Yet the safety risks related to aviation hinder the quick integration of drones into that ecosystem, broadening the gap between existing regulatory landscape and the exponentially growing popularity and ever-advancing technology of drones. The safety risks related to aviation hinder the quick integration of drones into the legislative ecosystem For the past several years, governments and legislators have been trying to tackle this problem by trying to answer two questions: how to properly integrate drones into the airspace without creating a hazardous impact on existing airborne operations, and how to enforce regulations in order to prevent the side-effects related to careless or malicious drone flights, taking into consideration public safety and physical security. Counter-UAS measures and regulations Up until 2018, legislators tried to tackle these two questions as a whole by introducing bundled legislation drafts covering the entire landscape of gaps they needed to address, which resulted in multi-parliamentary committee efforts both in the US and abroad to review and approve each bill - a process that is very slow by design. It was only in the beginning of this year that the issues were starting to be addressed separately: legislation related to limitations and counter-drone measures on the one hand, and legislation related to integration into airspace on the other. Let’s take a closer look at Counter-UAS (unmanned aerial systems) measures and what makes them challenging in terms of regulation. Over the past years, various counter-drone technologies have been introduced to enable control over rogue drones in order to either stop them from achieving their flight purpose or prevent them from creating safety hazards to people or property. These measures can be grouped into 3 types of technologies: Military grade solutions - including lasers and surface-air missiles Kinetic solutions - including net-guns and autonomous drones set out to catch the rogue drone and disable it airborne Non-kinetic RF-based solutions - aimed at either disabling, disrupting or accessing the drone’s communications channels in order to trigger a return-to-home function, or guide the drone into a safe landing route Aside from combat military operations, the legality of using the above technologies is questionable as they tamper with an airborne aircraft, might be considered as wiretapping and/or violate computer fraud laws. Therefore, one can conclude that unless changes to regulation are made, non-military facilities will continue to be defenceless from and vulnerable to rogue drones.  One can conclude that unless changes to regulation are made, non-military facilities will continue to be defenceless from and vulnerable to rogue drones European c-UAS legislation Next, let’s look at the state of c-UAS legislation in both Europe and US to better understand different legislative ecosystems and how they affect the possibilities of using counter drone measures. In the European Union, there is currently no uniform legislation, and the member countries rely on their own existing legal infrastructures. Roughly speaking, most countries use a method of exemptions to the communications and aviation laws to allow the use of counter drone measures after a close examination by the relevant authorities. Such exemptions are approved under scrutiny to particular sites, which provide some relief, but they do not allow broad use of countermeasures. Further discussion regarding a broader regulation change, on a country level or EU-wide, is only preliminary. US c-UAS legislation Preventing Emerging Threats - provides an initial infrastructure for counter drone measures to be used by various DoJ and DHS agenciesUnlike the EU, in the US exemptions are not possible within the existing legal framework, and the possible violation of US code title 18 means that the hands of both the government or private entities are tied when attempting to protect mass public gatherings, sports venues, or critical infrastructure. Therefore, it was more urgent to introduce legislation that would allow countermeasures to some extent. In September, US Congress approved the FAA-reauthorisation act for the next 5 years (H.R. 302), which was shortly after signed by the President and came into effect. Division H of the act - Preventing Emerging Threats - provides an initial infrastructure for counter drone measures to be used by various DoJ (Department of Justice) and DHS (Department of Homeland Security) agencies under strict limitations. However, the act avoids determining which technology the agencies should use, yet it requires minimal impact on privacy and overall safety in order to strike the necessary balance. This is the first profound counter-drone legislation and is expected to be followed by additional measures both in the US and in other countries. Updating counter-drone legal infrastructure In summary, 2018 has been a pioneering year for counter-drone legislation, and while technology already allows taking action when necessary, legal infrastructure needs further updates in order to close the existing gaps: covering additional federal assets, state-level governments, and private facilities of high importance, such as critical infrastructure sites. Legislators in the US and around the world need to continue working in a rapid tempo to keep up with the growing threat of drones. As with cars a century ago, the number of accidents will rise with the increase in time taken to regulate.

Video surveillance systems have proven to not only be a deterrent to crime, but are also now being used to collect data points to actually help detect abnormal behaviours which can alert authorities of potentially evolving situations. In either case, recorded video is critical for investigations to provide all but irrefutable evidence to prove or disprove that an incident took place and the identity of the individuals involved.  Sounds like a pragmatic approach that’s quite simple in theory. Not so when large numbers of cameras are deployed across multiple sites, and perhaps multiple users within the framework of a centrally managed system. Examples include a mass transit system, large university campus, mega shopping centres, airports, gaming resorts… and more.Video Management Systems have evolved from simply facilitating camera and recording management to a more sophisticated role Aside from the challenges presented with multiple camera feeds, recorders and control locations, and assuming that all system components are operating as they should; investigators need the right tools to find the footage they need.  Today’s advanced Video Management Systems (VMS) have evolved from simply facilitating camera and recording management to a more sophisticated role within a larger video surveillance and security system ecosystem.   Coordinated response efforts  From monitoring and tracking a live situation within a facility or across a municipality, to coordinating response efforts, through forensic investigations, new VMS capabilities provide a holistic solution that improves overall protection and contributes to business intelligence Among these capabilities, VMS forensic tools are meeting the needs of important investigation activity – both during and after an incident. For instance, while monitoring live video feeds, users can perform a basic investigation on individual cameras including playback, and digital and optical pan-tilt-zoom (for PTZ cameras), without the need to switch to a dedicated investigation mode. New VMS capabilities provide a holistic solution during the investigation process Advanced VMS solutions, such as OnSSI’s Ocularis, provide investigators with a multitude of options for accessing and enhancing video data to document incidents. Some of the most notable toolsets available include:  Switching between live and browse modes  As opposed to the Live Monitoring view, which displays multiple cameras asynchronously (i.e. different panes can show playback, paused and/or live cameras simultaneously), the Browse mode displays all cameras synchronously. This provides insight into events taking place at different locations at the time an incident occurred, allowing investigators to easily track an incident as it moves from camera to camera.   Scalable kinetic timeline Any video recordings triggered due to motion detection can be automatically queued to speed up the investigation process This provides a clear overview of recorded motion events over extended periods of time with the ability to scale the timeline to show shorter or longer time intervals. Colour-coded segments in the timeline indicate whether video has been recorded at a certain time to the minute, and whether motion was detected during those periods.  Any video recordings triggered due to motion detection can be automatically queued to speed up the investigation process. Once investigators isolate recorded video of interest using any of these preferences, one or multiple cameras can then be easily viewed.  Synchronous playback Synchronous playback displays all recorded video from all displayed cameras on the timeline into a Browse mode regardless of which video source is selected. This provides investigators with a comprehensive overview and fast access to all recorded video during the time intervals that an event occurred. One-click snapshot and motion detection  One-click snapshot allows investigators take JPEG image snapshots of any recorded video during the review process. These snapshots have multiple applications including identifying individuals of interest.  Motion detection eliminates the need to manually review volumes of recorded data Motion Detection expedites the event detection process by eliminating the need to manually review volumes of recorded video data. Motion can be detected within a defined zone, and the detection process can be configured to the exact parameters of the targeted behaviour or movement. The desired time intervals to search for motion detection can be preset by operators to expedite searches. The percentage of changed pixels can also be set when searching by motion detection to match the nature of the targeted movement. For example, high value setting would be used to detect a vehicle entering a detection region down to the size of a single parking space, eliminating false detections of persons walking in the parking lot. Additionally, parameters can be set for pixel colour and brightness to compensate for the amount of noise caused by ambient lighting, shadows, reflections and more.Motion Detection expedites the event detection process by eliminating the need to manually review volumes of recorded video data Slicing capabilities  Time Slicing enables users to easily and quickly access video of an incident by automatically generating equal-interval thumbnails of a specific camera view. Once the incident is evident in one of the thumbnails, investigators can create sets of thumbnails of increasingly smaller time intervals to determine the exact moment an incident began and/or ended. Motion Slicing is similar to a Time Slicer, but enables instant detection of motion events, adjusted for the duration of the event and level of motion.  Alert generators  Alerts generated by the recording component in response to events such as motion detection or generic events received from external systems (e.g. access control, emergency phones, etc.) may also be used in Time Slicing.  Event Sequencing provides messages generated by the recording components indicating a sequence of events. Each sequence may include one or more instances of motion, or alerts received from external sources, making this method more suitable for accessing complex incidents. Combined with the inherent video management and control capabilities provided by best in breed VMS solutions, these powerful new embedded investigation tools allow users with large surveillance systems to fully leverage the power of their investment to easily and comprehensively investigate events.

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