Smart Video surveillance systems have become important recently for ensuring public safety and security, especially in smart cities. However, applying real-time artificial intelligence technologies combined with low-latency notification and alarming has made deploying these systems quite challenging. This paper presents a case study for designing and deploying smart video surveillance systems based on a real-world testbed at a community college. We primarily focus on a smart camera-based system that can identify suspicious/abnormal activities and alert the stakeholders and residents immediately. The paper highlights and addresses different algorithmic and system design challenges to guarantee real-time high-accuracy video analytics processing in the testbed. It also presents an example of cloud system infrastructure and a mobile application for real-time notification to keep students, faculty/staff, and responsible security personnel in the loop. At the same time, it covers the design decision to maintain communities' privacy and ethical requirements as well as hardware configuration and setups. We evaluate the system's performance using throughput and end-to-end latency. The experiment results show that, on average, our system's end-to-end latency to notify the end users in case of detecting suspicious objects is 5.3, 5.78, and 11.11 seconds when running 1, 4, and 8 cameras, respectively. On the other hand, in case of detecting anomalous behaviors, the system could notify the end users with 7.3, 7.63, and 20.78 seconds average latency. These results demonstrate that the system effectively detects and notifies abnormal behaviors and suspicious objects to the end users within a reasonable period. The system can run eight cameras simultaneously at a 32.41 Frame Per Second (FPS) rate.

Researchers from Michigan State University have devised a way for synthetic faces to take a break from the deepfakes scene and do some good in the world – by helping image recognition systems to become more accurate. The new controllable face synthesis module (CFSM) they've devised is capable of regenerating faces in the style of real-world video surveillance footage, rather than relying on the uniformly higher-quality images used in popular open source datasets of celebrities, which do not reflect all the faults and shortcomings of genuine CCTV systems, such as facial blur, low resolution, and sensor noise – factors that can affect recognition accuracy. CFSM is not intended specifically to authentically simulate head poses, expressions, or all the other usual traits that are the objective of deepfake systems, but rather to generate a range of alternative views in the style of the target recognition system, using style transfer. The system is designed to mimic the style domain of the target system, and to adapt its output according to the resolution and range of'eccentricities' therein. The use-case includes legacy systems that are not likely to be upgraded due to cost, but which can currently contribute little to the new generation of facial recognition technologies, due to poor quality of output that may once have been leading-edge.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. The expanding use of surveillance cameras, whether in service of public safety, health monitoring or commercial operations, has heightened concerns about privacy. These days, it seems people's movements will be captured on CCTV cameras regardless of where they go. The number of surveillance systems in use has grown, with no signs of slowing down. According to the U.S. Bureau of Labor Statistics, the number of surveillance camera installations in the U.S. grew from 47 million to 85 million from 2015 to 2021, an increase of 80%.

A Chinese company is selling its surveillance technology to Iran's Revolutionary Guard, police, and military, according to a new report by IPVM, a surveillance research group. The firm, called Tiandy, is one of the world's largest video surveillance companies, reporting almost $700 million in sales in 2020. The company sells cameras and accompanying AI-enabled software, including facial recognition technology, software that it claims can detect someone's race, and "smart" interrogation tables for use alongside "tiger chairs," which have been widely documented as a tool for torture. The report is a rare look into some specifics of China's strategic relationship with Iran and the ways in which the country disperses surveillance technology to other autocracies abroad. Tiandy's "ethnicity tracking" tool, which has been widely challenged by experts as both inaccurate and unethical, is believed to be one of several AI-based systems the Chinese government uses to repress the Uyghur minority group in the country's Xinjiang province, along with Huawei's face recognition software, emotion-detection AI technologies, and a host of others.

Security cameras are a great way to keep an eye on commercial spaces without being on-site, especially after-hours. While security is a 24/7 business, most organizations can't afford to monitor their systems at all hours of the day and night. Hiring a third-party provider to monitor is expensive, and even with eyes on screens, human error still results in missed reports, slow response, and increased insurance and liability costs. However, new strides in cloud-based and AI technology are leveling the playing field for small and mid-sized organizations, and are making commercial video surveillance systems smarter than ever before. Recent advancements in AI-based video security have made this technology more powerful and accurate.

The rapid advancements in the technology of closed circuit television cameras and their underlying infrastructure has led to a sheer number of surveillance cameras being implemented globally, estimated to go beyond 1 billion by the end of the year 2021 . Considering the massive amounts of videos generated in real-time, manual video analysis by human operator becomes inefficient, expensive, and nearly impossible, which in turn makes a great demand for automated and intelligent methods for an efficient video surveillance system. An important task in video surveillance is anomaly detection, which refers to the identification of events that do not conform to the expected behavior. Abnormal events in the general sense have the characteristics of suddenness,in order to be able to understand the abnormal events in the first time, it usually takes a lot of manpower to stare at the monitoring screen for a long time to observe, so It will not only make people tired, but also easily overlook some inconspicuous events. Therefore, the automatic detection and recognition of abnormal events of surveillance video in complex scenes, as the core subject of intelligent video surveillance systems, is receiving more and more attention from researchers.

Video surveillance is commonly associated with security. But in most cases, it's used to record incidents and assist in investigations after the fact rather than prevent undesirable events. Artificial intelligence–powered video analytics is a highly promising trend that fundamentally changes the way things work. Extracting manageable data from a video stream can help recognize risky situations early on, minimizing damage and, ideally, completely avoid emergencies. At the same time, AI significantly expands the areas of application of video surveillance beyond security systems.

Artificial intelligence has vastly improved in the last decade to the point where AI-powered software has become mainstream. Many organizations, including schools, are adopting AI-powered security cameras to keep a close watch on potential threats. For example, one school district in Atlanta uses an AI-powered video surveillance system that can provide the current whereabouts of any person captured on video with a single click. The system will cost the district $16.5 million to equip around 100 buildings. These AI-powered surveillance systems are being used to identify people, suspicious behavior, guns, and gather data over time that will help identify suspects based on mannerisms and gait.

Prisons in Hong Kong are testing a variety of high-tech services that will allow correctional facilities to better track inmates, according to the South China Morning Post. The city's Commissioner of Correctional Services, Danny Woo Ying-min, claimed the new services will be used to monitor for abnormal behavior among the incarcerated, prevent self-harm, and operate the prisons more efficiently. The "smart prison" initiative includes strapping inmates with fitness tracker-style wristbands that monitor location and activity, including heart rate. Some facilities will also start to use video surveillance systems that can identify any unusual behavior, fights and attempts to inflict harm on one's self. Correctional Services is also testing robots that will be used to search for drugs in feces from inmates.

A number of vendors are seeking to provide next-generation gun detection capabilities to schools and other organizations. Responding to too-frequent reports of shootings in the U.S., technology firms are beginning to roll out systems designed to quickly detect firearms in public settings. Many of the systems offer an advantage over conventional metal detectors, given that they often include video surveillance that can spot a gun in plain sight in a crowd, surveillance a metal detector may not be able to duplicate. Plus, some of the systems can utilize facial recognition to identify known persons of interest, such as known sex offenders, gang members, and the like; another bit of surveillance currently beyond the reach of conventional metal detectors. The Canadian firm Patriot One Technologies, for example, offers a solution featuring a microwave radar scanner driven by artificial intelligence (AI) that can detect hidden weapons, along with a video surveillance component.