Dahua TechMonth features WizMind and 3 practical monitoring applications of human-based video metadata

Download PDF version Contact company

In a surveillance monitoring context, video metadata refers to the structured details that the user can extract from given video footage. It is data that provides information about other data – a “data about data” in short.

Human-based video metadata, as its name suggests, are data derived from human targets in the monitored scene. It can be utilised based on user’s requirements to significantly enhance the management and operation of various application scenarios, while carefully maintaining people’s privacy at the same time.

To give some insights into its practical usage, here are three conventional applications of human-based video metadata to adopt based during monitoring needs:

1) Searching targets

One of the most common applications of human-based video metadata is for target searching after an event. Intelligent surveillance devices, such as the Dahua WizMind products, offer a Quick Target Search function that can locate suspects in time using their physical attributes. Irrelevant targets in the captured images are filtered out by the back-end devices (IVSS, NVR) or by the platform

Target attributes include beard, glasses, mask, hat, etc., which are continuously being developed and increased to meet the requirements of various application scenarios. Irrelevant targets in the captured images are also filtered out by the back-end devices (IVSS, NVR) or by the platform.

It can capture up to 640* targets (with attributes) per second with an impressive detection rate of 98%*.

Generating statistics

In addition, another useful application of human-based video metadata technology is for target statistics. WizMind counts in real time the human targets in the monitored scene and filters them based on target category and direction.

The directions include A>B, B>A, or A<>B. And to top it up, it can also generate reports by year, month, and day. It can capture up to 96** objects per frame, providing a detection rate of 98%** and an accuracy rate of 96%**.

2) Detect PPE wearing

WizMind can detect wearing PPEs in construction sites including safety hats, protective vests, face masks, and glasses

Wearing personal protective equipment or PPE is a must on any construction site. Depending on the scope of the project, monitoring the proper wearing of PPEs on the site could be a tough challenge to implement.

The latest PPE Detection Technology of Dahua WizMind can detect wearing of commonly used PPEs in construction sites including safety hats, protective vests, face masks, and glasses. It can even detect the colour of the uniforms (top and bottom) of workers.

When a violation has been detected, the site supervisor or manager can be notified via the DMSS mobile app. Similar to target searching and statistics functions, it also offers a detection rate of 98%**, with an accuracy rate of 96%**.

3) Aid city road planning

Who would have thought that aside from vehicle-related data, statistics related to people on the street can also be useful in planning city roads? By collecting attributes of pedestrians, directions, and periods, local road planning departments can design better roads tailored for road users and commuters.

One good example is the Dahua solution developed for a local organisation in Ireland. Aside from motor vehicles and non-motor vehicles, metadata of pedestrians are also captured on main city roads and streets around plazas. These data are then securely transferred to an SFTP server for further data analysis. The data summary is relayed to the local transportation department to help them plan and manage streets more efficiently.

Key Takeaways

Human-based video metadata can be effectively utilised to optimise the monitoring operation of various application scenarios. Metadata based on human targets can be used to search suspects after an event, and to generate accurate statistics of the people in the monitored scene.

Also, these structured data can be used to detect proper wearing of PPEs in a particular site, and help local transportation departments in planning and maintaining city roads to benefit the general public.

Download PDF version Download PDF version

In case you missed it

How should the security industry promote diversity?

Diversity in a company’s workforce is arguably more important now than ever. Societal awareness of the importance of diversity has grown, and many people see diversity as an important factor that reflects positively (or negatively) on a company’s culture and image in the marketplace. We asked this week’s Expert Panel Roundtable: What should the security industry do to promote workplace diversity?

Why face recognition as a credential is the ideal choice for access control?

In the field of access control, face recognition has come a long way. Once considered too slow to authenticate people's identities and credentials in high traffic conditions, face recognition technology has evolved to become one of the quickest, most effective access control identity authentication solutions across all industries. Advancements in artificial intelligence and advanced neural network (ANN) technology from industry leaders like Intel have improved the accuracy and efficiency of face recognition. However, another reason the technology is gaining traction is due to the swiftly rising demand for touchless access control solutions that can help mitigate the spread of disease in public spaces. Effective for high volumes Face recognition eliminates security risks and is also virtually impossible to counterfeit Modern face recognition technology meets all the criteria for becoming the go-to solution for frictionless access control. It provides an accurate, non-invasive means of authenticating people's identities in high-traffic areas, including multi-tenant office buildings, industrial sites, and factories where multiple shifts per day are common. Typical electronic access control systems rely on people providing physical credentials, such as proximity cards, key fobs, or Bluetooth-enabled mobile phones, all of which can be misplaced, lost, or stolen. Face recognition eliminates these security risks and is also virtually impossible to counterfeit. Affordable biometric option Although there are other biometric tools available, face recognition offers significant advantages. Some technologies use hand geometry or iris scans, for example, but these options are generally slower and more expensive. This makes face recognition a natural application for day-to-day access control activities, including chronicling time and attendance for large workforces at construction sites, warehouses, and agricultural and mining operations. In addition to verifying personal credentials, face recognition can also identify whether an individual is wearing a facial covering in compliance with government or corporate mandates regarding health safety protocols. Beyond securing physical locations, face recognition can also be used to manage access to computers, as well as specialised equipment and devices. Overcoming challenges with AI So how did face recognition become so reliable when the technology was once dogged by many challenges, including difficulties with camera angles, certain types of facial expressions, and diverse lighting conditions? Thanks to the emergence of so-called "convolutional" neural network-based algorithms, engineers have been able to overcome these roadblocks. SecurOS FaceX face recognition solution FaceX is powered by neural networks and machine learning which makes it capable of authenticating a wide range of faces One joint effort between New Jersey-based Intelligent Security Systems (ISS) and tech giant Intel has created the SecurOS FaceX face recognition solution. FaceX is powered by neural networks and machine learning which makes it capable of authenticating a wide range of faces and facial expressions, including those captured under changing light, at different resolution levels, and varying distances from the video camera. Secure video management system A common face recognition system deployment begins with IP video cameras that feed footage into a secure video management system connected to a video archive. When the software initially enrolls a person’s face, it creates a "digital descriptor" that is stored as a numeric code that will forever be associated with one identity. The system encrypts and stores these numeric codes in a SQL database. For the sake of convenience and cost savings, the video server CPU performs all neural network processes without requiring any special GPU cards. Unique digital identifiers The next step involves correlating faces captured in a video recording with their unique digital descriptors on file. The system can compare newly captured images against large databases of known individuals or faces captured from video streams. Face recognition technology can provide multi-factor authentication, searching watchlists for specific types of features, such as age, hair colour, gender, ethnicity, facial hair, glasses, headwear, and other identifying characteristics including bald spots. Robust encryption SED-compatible drives rely on dedicated chips that encrypt data with AES-128 or AES-256 To support privacy concerns, the entire system features an encrypted and secure login process that prevents unauthorized access to both the database and the archive. An additional layer of encryption is available through the use of Self-Encrypting Drives (SEDs) that hold video recordings and metadata. SED-compatible drives rely on dedicated chips that encrypt data with AES-128 or AES-256 (short for Advanced Encryption Standard). Anti-spoofing safeguards How do face recognition systems handle people who try to trick the system by wearing a costume mask or holding up a picture to hide their faces? FaceX from ISS, for example, includes anti-spoofing capabilities that essentially check for the "liveliness" of a given face. The algorithm can easily flag the flat, two-dimensional nature of a face mask, printed photo, or image on a mobile phone and issue a "spoof" alarm. Increased speed of entry Incorporating facial recognition into existing access control systems is straightforward and cost-effective Incorporating facial recognition into existing access control systems is straightforward and cost-effective. Systems can operate with off-the-shelf security cameras and computers. Users can also leverage existing infrastructure to maintain building aesthetics. A face recognition system can complete the process of detection and recognition in an instant, opening a door or turnstile in less than 500ms. Such efficiency can eliminate hours associated with security personnel checking and managing credentials manually. A vital tool Modern face recognition solutions are infinitely scalable to accommodate global enterprises. As a result, face recognition as a credential is increasingly being implemented for a wide range of applications that transcend traditional access control and physical security to include health safety and workforce management. All these capabilities make face recognition a natural, frictionless solution for managing access control, both in terms of performance and cost.

What are the challenges and benefits of mobile access control?

There is a broad appeal to the idea of using a smartphone or wearable device as a credential for physical access control systems. Smartphones already perform a range of tasks that extend beyond making a phone call. Shouldn’t opening the door at a workplace be among them? It’s a simple idea, but there are obstacles for the industry to get there from here. We asked this week’s Expert Panel Roundtable: What are the challenges and benefits of mobile access control solutions?