The BallistiScan School Metal Detector by Ballistiglass.

We've discovered a calm and comfortable way to screen for guns at schools while at the same time not altering the academic environment.
It's possible to use metal detectors in schools without it being scary or having the feel of a prison.

BallistiScan is small, portable, and very easy to use. The feedback from the students and the staff is phenomenal. 
BallistiScan establishes a deterrent against bringing weapons on campus and at the same time, everyone feels a bit safer.
The complete system is less than $5000 and is already installed in schools. 

Visit www.ballistiglass.com to learn more about BallistiScan and read the white paper on school metal detection (it's 4 pages). 
BallistiScan is one layer of security that is designed to act as a deterrent, not a panacea for school security. 
Security is derived from multiple layers of security that fall within the 4D's of security: 
(D)eterrence, 
(D)etect, 
(D)eny, and 
(D)efeat. 
The metal detector acts as a (D)eterrent.  

Cameras with intelligent software act as the (D)etect. Access control (locks, single point of entry, etc) and bullet resistant glass (e.g. BallistiMax) act as (D)eny. Finally, it usually takes an armed individual to (D)efeat the gunman. Each (D) helps reduce the probability of a fatality in the event of a mass shooting. Unfortunately, it may very well be impossible to reach a zero probability, but each layer definitely contributes significantly to this goal. That is why there is no single answer to school security- there are answers. These answers comprise the layers of security that a gunman will encounter.

here are several options to consider when it comes to analytics for video surveillance systems: deploy analytics on the edge, or “in-camera”; use a dedicated server; or use a hybrid approach which leverages both edge and server implementations.


The Power at the Edge
As more powerful in-camera chipsets are developed, edge devices continue to evolve. They are capable of utilizing powerful analytics that can inform operators on a wide range of real-time video or audio events requiring attention. Some cameras include video analytics like loitering, line crossing, face detection, enter/exit and direction detection. Audio classification can warn of glass breaks, gunshots and screams. More sophisticated analytics like queue management and heat maps bring new opportunities for business and traffic intelligence.
The next step in the evolution will come from a form of AI (Artificial Intelligence) called Deep Learning. Deep learning takes machine learning to a new level based on neural network theory that mimics the complexity of the human brain. Previously only available as a server-side process which would require video to be decompressed and processed, algorithms and companion data sets created from deep learning can be embedded on the edge to perform real-time detection and alerts for the specific conditions they have been trained to recognize. While edge devices cannot continue to self-learn, they can provide a useful function for recognizing known objects or behaviors. Still in its infancy, this technology will take in-camera analytics to a new level for both security and retail use cases.


Servers Have an Important Role to Play
Even with the increased analytic power available on the edge, there are certain tasks that benefit from running on a powerful server. As an example, license plate recognition demands more than optical character recognition, it also requires an interface to a large database where plates can be stored, cross-referenced and flagged. Likewise, facial recognition requires sophisticated database analytics for the same reasons. Dedicated servers are more expensive than edge devices with in-built analytics, so it’s important to clearly understand the needs of a business – Do we need to detect an object or a specific person? For now, machine learning and deep learning algorithms that continuously learn require the computational horsepower and storage that only a server can provide.


Bandwidth Limits – Server vs. Edge
As powerful as servers are, they do have limits. If a server is going to analyze video, it must first decode it, which consumes a fixed amount of CPU/GPU resources. After the video is decoded, then it can be analyzed. As the video channel count grows, it’s possible to saturate even a powerful server very quickly. Edge devices are immune to this problem because analytics are typically run right after the image is captured and before it is encoded into an IP video stream. The results of the analytics are then passed as data to the VMS, NVR or messaging system as metadata, which is quite small compared to a video stream.


Enter the Hybrid
For the reasons above, you can imagine that for powerful analytics processes, such as number plate or facial recognition, an efficiency can be derived from combining edge analytics with server-side processing and database management.
This hybrid approach is already in use by some vendors as a way to deliver powerful analytics at a fraction of the previous cost. In order to enable this, some camera manufacturers have reserved space on their cameras to allow third-party plugin analytics to be installed which pass data directly to the server. The video doesn’t need to be decoded which saves precious CPU/GPU cycles. This represents an ideal scenario that uses the best of both worlds: Edge analytics harvesting data before video is encoded, and servers receiving lightweight metadata from the edge to perform analysis. Whereas a single server might be able to decode and analyze 20 channels of video, the same server processing metadata provided by the cameras could analyze over 250 channels. The cost savings of such an implementation is substantial.


Conclusion
The edge will continue to deliver compelling analytics capabilities that suit a majority of use cases. The evolution of AI and deep learning will accelerate both edge and server capabilities to new levels in our industry. For the most demanding analytics, database processes and AI implementations, a hybrid approach can yield the best business value.

https://www.huffingtonpost.kr/entry/story_kr_5c024372e4b0606a15b5748f

ccording to Yonhap News, 

(SEOUL=Yonhap News) The Ministry of Trade, Industry and Energy will hold a public hearing on August 7 at COEX to collect feedback from the public on the service and investment FTA negotiations with Russia, said the Ministry. 

The hearing is to take advice from stakeholders and experts before the negotiation according to the Act on the Conclusion Procedure and Implementation of Commercial Treaties. 

South Korea and Russia agreed to start the procedure of FTA negotiations related to service and investment sector during the President Moon's visit to Russia in June.

The Korean Government expects that the free trade will enable Korea to occupy service market in Russia, which has been on the rise, and to see increased mutual investment of businesses from both countries by creating stable investment environment and stronger investor protection. 

Presentations at the public hearing on FTA's economic validity, expected effects and task will be followed by a comprehensive discussion and a question and answer session. 

Cheon-il Park, Head of Center for Trade Studies and Cooperation, Korea International Trade Association, Seon-hwa Kim, Director of Trade Cooperation Department of KOTRA, Jong-deok Kim, Head of Trade and Investment Policy Team of KIEP, Professor Mun-seong Kang of Korea University and industry officials will participate as discussion panels. 

The government plans to report to the National Assembly and implement follow-up procedure to begin negotiation taking into consideration the opinions from the public hearing and stakeholders. 

bluekey@yna.co.kr


Copyrights Yonhap News. All Rights Reserved.
Reprint or redistribution without permission is prohibited.

Source Text

** This article was translated from Korean.
Source: Yonhap News (August 6, 2018)

http://www.investkorea.org/en/business/fta.do

지자체 영상정보,
이용·위탁 기준은 어떻게 될까
 
개인정보보호위원회 영상정보 이용·위탁 관련 의결사항 2건 점검
 
울산광역시와 경기도 광명시가 영상정보 이용 및 위탁에 관한 사항을 개인정보보호위원회에 문의했다. 울산광역시는 재난관측 목적으로 수집된 영상정보 이용 가능 여부를, 광명시는 광명경찰서에 영상정보 위탁이 가능한지 여부를 질의했다. 하지만 두 지자체의 질의 요청에 대한 개인정보보호위원회의 의결은 서로 달랐다. 울산광역시에 대해서는 이용 가능하다는 답변을, 광명시는 광명경찰서에 위탁해선 안 된다는 결론을 내렸다.
글 김경애 기자
 
울산광역시, 재난관측 영상정보 통합방위종합상황실에서 이용 가능
울산광역시는 ‘재난관측을 목적으로 설치한 영상정보와 울산광역시 구·군에서 산림·방범·교통·홍수 관리 등을 목적으로 설치한 영상정보를 통합방위종합상황실에서 이용하고, 제공받을 수 있는지 여부에 대해 질의를 요청했다.
이에 대해 개인정보보호위원회는 “울산광역시는 통합방위사태가 선포된 지역의 통합방위작전 지원을 위해 통합방위종합상황실에서 본 건의 영상정보를 이용하거나 구·군으로부터 제공받을 수 있다”는 의결을 내렸다.
다만, 울산광역시는 그 전에 ‘개인정보보호법’ 제58조 제4항에 따라 목적을 위해 필요 범위에서 최소한의 기간에 최소한의 개인정보만을 처리해야 한다. 또한, 개인정보의 안전한 관리를 위해 필요한 기술적·관리적·물리적 보호조치, 개인정보의 처리에 관한 고충처리, 그 밖에 개인정보의 적절한 처리를 위해 필요한 조치를 마련해야 한다는 단서를 달았다.
 
광명시 통합관제센터 영상정보, 광명경찰서에 위탁 안 돼
경기도 광명시는 통합관제센터에서 수집한 영상정보의 관계기관 제공(열람 및 반출) 업무를 광명경찰서에 위탁하는 것이 ‘개인정보보호법 제26조’에 따라 가능한지 여부를 질의 요청했다.
이에 대해 개인정보보호위원회는 “광명시는 경찰서 등 관계기관에 영상정보를 제공하는 업무를 광명경찰서에 위탁할 수 없다”는 결론을 내렸다.
경기도 광명시는 2010년 통합관제센터를 개소해 범죄예방과 주정차단속 등을 목적으로 총 2,538대의 영상정보처리기기를 설치·운영하고 있다. 경기도 광명시가 통합관제센터에서 수집하는 영상정보는 범죄 수사 등을 위해 광명경찰서에 제공하는 것이 대부분을 차지하고 있다. 이는 광명경찰서가 자신의 업무(범죄수사, 교통민원 등)를 처리하기 위해 광명시로부터 영상정보를 제공받는 제3자의 지위에 있는 자라고 할 수 있고, 개인정보 처리에 관해 독자적인 이익을 가지지 않고 개인정보처리자의 관리·감독 아래 위탁받은 범위 내에서만 개인정보를 처리하는 수탁자라고 보기는 어렵다는 의견에 기반한 것이다.
이에 대해 개인정보보호위원회는 “광명경찰서가 광명시의 수탁자가 될 경우 광명경찰서는 실질적으로 영상정보를 제공하는 개인정보처리자와 영상정보를 제공받는 제3자의 지위를 겸하게 된다”며 “결국 개인정보를 제공받는 제3자의 입장에서 필요성만을 근거로 개인정보를 제공받을 수 있게 하는 결과”라고 해석했다. 또한, 정보주체의 개인정보자기결정권 등을 보호하고자 하는 ‘개인정보보호법’의 입법취지에 비추어 허용될 수 없다고 덧붙였다.
 
시큐리티월드 2018.11월호
 

출처:https://www.prnewswire.com/news-releases/dahua-technology-launches-integration-partner-program-dipp-at-security-essen-2018-300718283.html

HANGZHOU, China, Sept. 25, 2018 /PRNewswire/ -- Dahua Technology, a leading solution provider in the global video surveillance industry, advocates openness and believes that an industry ecosystem can benefit all industry players. At Security Essen 2018, it launches the Dahua Integration Partner Program (DIPP), aiming to extend collaboration with 3rd party partners in technology integration and business development.

DIPP focuses on creating comprehensive and integrated solutions with 3rd party partners for wider customer and end-user base. Featuring three partner levels - Strategic Partner, Golden Partner and Silver Partner - DIPP ensures that its partners enjoy multiple benefits through Dahua Technology's global network. Partners who specialize in Video Management Software, Access Control System, Video Analytics, Physical Security Information Management, Alarm Systems, Cloud-based Services and other Vertical Systems that involve Security Surveillance are most welcome to join the program.
DIPP benefits partners on both technical and business sides, including in-depth marketing, project and technical support. Marketing-wise, DIPP partners enjoy dedicated marketing support including co-marketing opportunities at Dahua Technology's global events, recommendation in Dahua 3rd-party-partner-solution catalog, etc. Technical-wise, Dahua Technology guarantees feedback within 24h, dedicated technical support, opportunities for sample borrowing, roadmap sharing & product introduction, and free training regarding Dahua Technology integration protocols. The key is that Dahua Technology ensures considerable benefits at project parts, including end-to-end support, opportunities for projects pipelines sharing and product recommendation, etc.
So far, some 3rd party partners have joined DIPP, like Ivideon, AxxonSoft, to name just a few. "We're happy to launch DIPP, a program aiming to better serve our partners and achieve win-win results." said James Wang, Deputy General Manager of overseas business at Dahua Technology.
"We were the first to implement a solution on Dahua Open Platform, and we're very happy to be among the first companies to participate in the Dahua Integration Partner Program. Dahua Technology's innovative technologies, particularly its development of deep-learning video analytics, completely aligns with our vision of how video surveillance should develop. I'm confident that, together, we will create products that grow our value proposition for both our partners and clients." Said Alan Ataev, Global Sales Director at AxxonSoft.
For detailed information, please visit DIPP website portal. With a mission of "Enabling a Safer Society and Smarter Living", Dahua Technology will continue to focus on "Innovation, Quality, and Service" to serve partners and customers around the world.
SOURCE Dahua Technology


​Related Links

http://www.dahuasecurity.com

4. 연결된 모든 것들로 인한 공포, 'IoT 보안 위협'
​
2018년도 사이버공격 전망에 따르면 IoT 봇넷이 지능적으로 진화하여 대규모의 공격이 이루어질 수 있고, 스마트카, 드론 등 새로운 스마트기기의 등장으로 인해 새로운 보안위협으로 이어질 수 있다고 합니다. 특히, 해킹으로 인해 스마트카를 급발진, 급정지시켜 인명사고로까지 이루어질 수 있기때문에 보안강화에 대한 중요성이 더욱 커지고 있습니다.

출처: KISA 한국인터넷 진흥원

1. EU, 일반개인정보보호규정(GDPR) 본격시행, 2018년 5월 25일​ 본격 시행

일반개인정보보호규정(General Data Protection Regulation, GDP)은 유럽 시민 대상으로 비즈니스를 하거나 유럽 거주자의 개인정보를 다룰 경우 개인 프라이버시에 대해 법으로 강제한다는 것입니다. 이를 위반하면 높은 과징금이 부여된다고 하니 유럽과 비즈니스를 하는 여러 기업에 상당한 영향을 주게 될 것 같습니다.

2. 데이터에 미래가 있다, 디지털 결제 시대의 원유, Data!

4차 산업 혁명으로 인해 데이터를 어떻게 활용하느냐가 중요하며 세계는 데이터 경제(Data Economy)* 시대로 접어들고 있습니다. 특히, 인공지능 서비스를 위해 많은 데이터를 수집하고 활용하는 과정에서 개인정보 및 사생활 침해 이슈가 있을 수 있고 우리나라의 개인정보 활용에 대한 소극적 입장으로 인해 데이터의 안전한 활용조차도 어렵다는 업계의 의견이 있습니다. 사생활이 침해되지 않으면서 이를 유용히 활용할 수 있는 법안을 방안하는 것이 시급합니다.
*  데이터 경제: 데이터에 접근하고 활용하기 위해 데이터 생산, 인프라 제공, 연구조사 등 서로 다른 역학을 담당하는 구성원으로 이루어진 생태계

3. Privacy by Design! 개인정보 활용은 정보주체의 안심이 우선

Privacy by Design이란 온라인 서비스 기획부터 폐기까지의 전 생애주기(life cycle)에 걸쳐 이용자의 프라이버시와 데이터를 보호하는 것을 염두하여 기획하고 이에 대한 기술 및 정책을 적용하는 것을 말합니다. 사후 대응이 아닌 예방에 초점을 두고 있습니다. 개인정보보호 적용설계를 위해서 다양한 기술 및 구체적인 적용방안이 마련되고 있습니다. 

4. 데이터 무역 활성화! 개인정보 국외이전/데이터 국지화 제도정비 시급

'개인정보 국외 이전'과 '데이터 국지화 제도'의 입장이 양립하고 있습니다. 즉, 개인정보를 지키기 위해 데이터센터를 국외에 둘 것인지 국내에 둘 것인지에 대한 입장이 양립하는데 이에 대한 균형점과 국가별 관련 규제가 상이하여 이에 대한 대응방안을 강구할 필요가 있습니다.

5. 개인정보 안전한 활용, 4차 산업혁명 선도의 핵심

사물인터넷(IoT) 보급 확대 등으로 인해 전 분야에서 다양한 가치를 창출할 수 있게 됐습니다. 그러나 보안상 취약점을 노린 개인정보 유출행위도 증가할 수 있으므로 개인정보 보호를 위한 관련 법과 제도를 정비할 필요가 있습니다.

6. 사업장 감시 vs  근로자 프라이버시

감시장비가 대중화되면서 프라이버시에 보호에 대한 중요성도 대두되고 있고 특히, 사업장에서 근로자들의 불만과 불편도 증가되어 사업자와 근로자 간의 조율이 필요합니다. 감시 설비 도입 시에 사전에 충분한 설명이 필요하며 절차 및 방법에 투명성이 보장되지 않을 때에는 이를 강제할 수 있는 절차를 도입하고 있습니다. 

7. 바이오정보 빅데이터 시대, 커지는 개인정보 침해 위협

바이오정보의 범위가 예전에는 지문, 사진, DNA에 미쳤다면 요즘에는 홍채, 혈관 형태, 음성, 망막, 걸음걸이, 필적 등 사람의 거의 모든 생물학적 또는 신체적인 특징과 행동까지도 포함하고 있습니다. 이에 대한 개인정보의 중요성은 꾸준히 제기되어 왔으나 아직 법령에만 의존할 뿐 뚜렷한 보호 장치는 마련되어 있지 않습니다.  이에 대해 바이오정보 활용에 대한 명확한 근거와 법률을 더욱 강화해야 합니다.

IHS Top Video Surveillance Trends For 2018:
 
AI (artificial intelligence) - computers being able to perform specific tasks as well as or better than human intelligence. In the context of video surveillance, AI is used in the field of computer vision to classify visual images and patterns within them.

Big data – huge amounts of different information being stored, organized and analyzed by computers to identify trends, patterns, and relationships. In the context of video surveillance, the data could be metadata describing hours of video surveillance footage combined with other data sources to highlight patterns relating to security or business operations.

Cloud computing – instead of using a local server to store or manage video surveillance data, using a network of internet-connected remote servers. Generally this network has the ability to provide additional resource if and when required from a larger available pool. The available resource may be clustered into a datacenter or network of datacenters. These may be private (entirely or partly owned for exclusive use by specific organization/s) or public (resource accessible to multiple separate users).

Deep learning – a branch of machine learning and subset in the field of AI. Deep learning makes use of algorithms to structure high-level abstractions in data by processing multiple layers of information, emulating the workings of a human brain (a neural network).

Edge computing/storage - performing data processing and analytics/storage closest to the source of the data (normally, in this context, in a video surveillance camera).

Face recognition – when a video surveillance system can automatically match a person’s face against a database of individuals.

GPU (graphics processing unit) - A programmable chip specialized for use in image processing. Due to the requirement to be able to simultaneously processing multiple large data blocks required in modern image processing GPUs have been found to be highly suitable for deep learning/neural network processing.

H.265 – (or MPEG-4 part 2) is a video compression codec standard approved by the International Telecommunications Union (ITU-T). Compared with H.264, H.265 has the potential to use 30–40% less bandwidth for a video stream of the same quality.

Forensic video analytics as a service - IHS Markit expects that forensic video analytics will be integrated into existing cloud services. For example in the body-worn camera market many police forces already utilize the cloud to store and review body-worn video, yet, in these repositories we still see a degree of separation from other video sources, for example from fixed (public or private) video surveillance.