With the continuous increase in the number and resolution of video surveillance cameras, the burden of transmitting and storing surveillance video is growing. Traditional communication methods based on Shannon's theory are facing optimization bottlenecks. Semantic communication, as an emerging communication method, is expected to break through this bottleneck and reduce the storage and transmission consumption of video. Existing semantic decoding methods often require many samples to train the neural network for each scene, which is time-consuming and labor-intensive. In this study, a semantic encoding and decoding method for surveillance video is proposed. First, the sketch was extracted as semantic information, and a sketch compression method was proposed to reduce the bit rate of semantic information. Then, an image translation network was proposed to translate the sketch into a video frame with a reference frame. Finally, a few-shot sketch decoding network was proposed to reconstruct video from sketch. Experimental results showed that the proposed method achieved significantly better video reconstruction performance than baseline methods. The sketch compression method could effectively reduce the storage and transmission consumption of semantic information with little compromise on video quality. The proposed method provides a novel semantic encoding and decoding method that only needs a few training samples for each surveillance scene, thus improving the practicality of the semantic communication system.

Surveillance systems often struggle with managing vast amounts of footage, much of which is irrelevant, leading to inefficient storage and challenges in event retrieval. This paper addresses these issues by proposing an optimized video recording solution focused on activity detection. The proposed approach utilizes a hybrid method that combines motion detection via frame subtraction with object detection using YOLOv9. This strategy specifically targets the recording of scenes involving human or car activity, thereby reducing unnecessary footage and optimizing storage usage. The developed model demonstrates superior performance, achieving precision metrics of 0.855 for car detection and 0.884 for person detection, and reducing the storage requirements by two-thirds compared to traditional surveillance systems that rely solely on motion detection. This significant reduction in storage highlights the effectiveness of the proposed approach in enhancing surveillance system efficiency. Nonetheless, some limitations persist, particularly the occurrence of false positives and false negatives in adverse weather conditions, such as strong winds.

Fox News correspondent Stephanie Bennett joins'Fox News Live' to break down recent evidence tying missile fragments in Russian attacks to North Korea. North Korea is putting surveillance cameras in schools and workplaces and collecting fingerprints, photographs and other biometric information from its citizens in a technology-driven push to monitor its population even more closely, a report said Tuesday. The state's growing use of digital surveillance tools, which combine equipment imported from China with domestically developed software, threatens to erase many of the small spaces North Koreans have left to engage in private business activities, access foreign media and secretly criticize their government, the researchers wrote. But the isolated country's digital ambitions have to contend with poor electricity supplies and low network connectivity. Those challenges, and a history of reliance on human methods of spying on its citizens, mean that digital surveillance isn't yet as pervasive as in China, according to the report, published by the North Korea-focused website 38 North. The study's findings align with widely held views that North Korean leader Kim Jong Un is stepping up efforts to tighten the state's control of its citizens and promote loyalty to his regime.

This article presents an AI-enabled Smart Video Surveillance (SVS) designed to enhance safety in community spaces such as educational and recreational areas, and small businesses. The proposed system innovatively integrates with existing CCTV and wired camera networks, simplifying its adoption across various community cases to leverage recent AI advancements. Our SVS system, focusing on privacy, uses metadata instead of pixel data for activity recognition, aligning with ethical standards. It features cloud-based infrastructure and a mobile app for real-time, privacy-conscious alerts in communities. This article notably pioneers a comprehensive real-world evaluation of the SVS system, covering AI-driven visual processing, statistical analysis, database management, cloud communication, and user notifications. It's also the first to assess an end-to-end anomaly detection system's performance, vital for identifying potential public safety incidents. For our evaluation, we implemented the system in a community college, serving as an ideal model to exemplify the proposed system's capabilities. Our findings in this setting demonstrate the system's robustness, with throughput, latency, and scalability effectively managing 16 CCTV cameras. The system maintained a consistent 16.5 frames per second (FPS) over a 21-hour operation. The average end-to-end latency for detecting behavioral anomalies and alerting users was 26.76 seconds.

AI strategist Lisa Palmer and privacy consultant Jodi Daniels discuss privacy concerns around the acquisition of biometric data. A significant expansion in Artificial intelligence (AI) facial recognition technology is increasingly being deployed to catch criminals, but experts express concern about the impact on personal privacy and data. According to the Allied Market Research data firm, the facial recognition industry, which was valued at $3.8 billion in 2020, will have grown to $16.7 billion by 2030. Lisa Palmer, an AI strategist, said it is important to understand that an individual's data largely feeds what happens from an AI perspective, especially within a generative framework. While there has been data recorded on citizens for decades, today's surveillance is different because of the quantity and quality of the data recorded as well as how it's being used, according to Palmer.

This month, French lawmakers are expected to pass legislation for the 2024 Paris Olympics, which, for the first time in France's history, will permit mass video surveillance powered by artificial intelligence (AI) systems. When governments embark on the slippery slope towards the expansion of surveillance powers, it has damning consequences for fundamental human rights, including the rights to privacy, equality and non-discrimination, as well as freedom of expression and peaceful assembly. Under the guise of ensuring security and fighting terrorism, the French authorities will be able to monitor the movements of millions of people from around the world, whether they are heading to or near stadiums, or using public transportation leading in or out of the premises of the grand sporting event. The need for security during the game is understandable, but transparency and legal justification are needed at every step of the way. Any proposal concerning security must comply with fundamental rights.

With the advancement of vision-based artificial intelligence, the proliferation of the Internet of Things connected cameras, and the increasing societal need for rapid and equitable security, the demand for accurate real-time intelligent surveillance has never been higher. This article presents Ancilia, an end-to-end scalable, intelligent video surveillance system for the Artificial Intelligence of Things. Ancilia brings state-of-the-art artificial intelligence to real-world surveillance applications while respecting ethical concerns and performing high-level cognitive tasks in real-time. Ancilia aims to revolutionize the surveillance landscape, to bring more effective, intelligent, and equitable security to the field, resulting in safer and more secure communities without requiring people to compromise their right to privacy.

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%.

Artificial intelligence (AI) is a cutting-edge technology that is being adopted by forward-thinking businesses. The concept of artificial intelligence, on the other hand, has been around for decades. In 1955, "A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence" was published, which coined the term "artificial intelligence." Dartmouth University sponsored the first AI research project in 1956, which is widely regarded as the start of artificial intelligence. So, why is AI gaining popularity now, more than sixty years later?

The future of A.I. is … analog? At least, that's the assertion of Mythic, an A.I. chip company that, in its own words, is taking "a leap forward in performance in power" by going back in time. Before ENIAC, the world's first room-sized programmable, electronic, general-purpose digital computer, buzzed to life in 1945, arguably all computers were analog -- and had been for as long as computers have been around. Analog computers are a bit like stereo amps, using variable range as a way of representing desired values. In an analog computer, numbers are represented by way of currents or voltages, instead of the zeroes and ones that are used in a digital computer.