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Aug 31, 2021
7 minute read
Artificial intelligence (AI) is the technology where marketers anticipate the most growth over the next two years. The benefits of such development will be twofold. Firstly, AI offers a means of saving time and becoming more productive. Marketing insights, automation, and execution will allow marketers to stimulate better business results and spend more time on strategy and creative development. AI offers marketers a way to scale personalization in their marketing execution without straining their teams. It also gives marketers new ways to provide value to their customers and build lifetime value models, making them more relevant. AI gives marketers a richer user understanding, making them smarter and more effective.
What Is AI?
Artificial Intelligence is a branch of Computer Science concerned with building machines capable of mimicking human behavior. However, AI generally does require specialized software and hardware components for both learning the data and extracting insights from it.
This is done in three stages:
1. Learning Process
This generally involves gathering a lot of data and converting them to rules for actionable information.
2. Reasoning Process
This involves choosing the right algorithm to reach a particular outcome.
3. Self–Correction Process
This part mainly aims to continually improve the performance by minimizing its errors.
What is Computer Vision?
Recent improvements in specialized hardware and extensive research in Deep learning has given rise to a field of AI called Computer Vision. Computer Vision helps a computer to understand, identify and process images in such a way that it mimics human behavior. This has made it possible for smart cameras, also known as AI cameras, to exist. The AI cameras can process and extract meaningful insights from video feed in real-time.
What Can Computer Vision Do?
• Image Classification
Label and categorize images to a pre-defined class such as animal, human, food etc. An image of the said object is given to the Computer Vision Algorithm which then returns the label of the image based on the historical trends and weights.
• Object Localization
Locate an object in an image and mark it (normally with a bounding box). An image is given to the model with multiple objects, the marked bounding boxes are returned as a response from the algorithm.
• Object Detection
Locate and categorize various objects in an image. This is a combination of image classification and localization where the image is first processed using object localization and then given to an image classification algorithm for categorizing them into various classes.
This involves segmenting the entire image into various parts to understand what object they belong to. Two major categories are:
• Semantic Segmentation
Pixel-level classification is done, classifying each pixel into different objects. Each instance of the class is categorized into a different class.
• Instance Segmentation
Unlike semantic segmentation, instance segmentation categorizes each pixel into different classes. Each instance of an object is classified as one rather than being separate for each.
• Key Point Detection
This involves detecting and people and identifying their key points. These key points are then used to identify poses and human behavior. Input is given to the Keypoint extraction algorithm, it returns the mapped landmarks and the pose/action being performed.
AI cameras and Surveillance
The rise of computer vision and specialized hardware has enabled CCTV cameras to become smarter. These smart cameras can be used to monitor activities within a certain area and extract useful insights from the video footage. These insights are then used to perform a specific action – for example call, police in case robbery is going on.
Is There A Need For Video Surveillance?
This is a question some may find asking themselves, is it worth the time and money I put into this? Here are some benefits of using smart cameras for video surveillance.
Whether it be your business or some private space, video surveillance and AI Cameras are a must for security. Not only does the AI Camera help in case an attack has occurred but can also be used as a preventive measure for the attack. Imagine knowing that a robber is approaching your porch or if someone is trying to steal something. AI cameras can detect human behavior and alerting you or the proper authorities regarding it!
Moreover, AI cameras can be used to monitor customer behavior in your business as well. Imagine knowing how many people are in a queue and how long they’ve been there for better queue management or when your coffee machine needs a refill based on usage.
It is both cost-effective and scalable, with the expansion in area, adding new smart cameras and integrating them into the current system is relatively easier. Therefore, you don’t have to worry about your safety as you expand your business.
Using motion detection in these smart cameras not only reduces storage consumption by saving footage only when motion is detected in specified areas but also be used to identify suspicious activity. Unwanted motion can be detected at undesignated areas and times.
How is Video Surveilance done?
It is required to first set up the video surveillance system. The cameras need to be connected to specialized hardware and software to run Computer Vision Algorithms and databases for storing key insights.
Using the smart camera, live feed is transmitted to specialized hardware for processing. They contain specialized Computer Vision algorithms for extracting required insights from the live feed being. Once the algorithm extracts key insights, they’re sent over to the database for further processing.
Ethics and Privacy
While AI and Surveillance systems come with a lot of benefits, it is imperative to keep in view the ethical and privacy concerns that may arise with the use of data in AI. Here are some of the things that must be considered while processing data.
- There must be consent between both the data service and the data provider regarding use of data.
- There must be clarity in what the data will be used for between both parties.
- Users should have the option to remove the data from the system and prevent it from being processed.
- Personally, identifiable information must not be processed in AI without the consent of the person.
These are just some, of the many, considerations that must be kept in mind when processing data.
Smart Cameras And Video Analytics Across Different Industry Verticals
Recent technologies in the market trends have obliged the significant need for viable solutions to intelligent video cameras that are nothing but intelligent video analytics.
Today in different industry verticals such as retail, QSR (Quick Service Restaurants), Co-working space, and smart buildings, intelligent video analytics has gone beyond the traditional security and loss prevention by providing retailers/QSR insightful business intelligence such as store traffic statistics and queue data, service time, purchase behavior, and various other insights.
This provides accurate and reliable information by monitoring continuously through many smart cameras events that human operators or employees can overlook. The analytics utilizes the video system architecture employed, to enhance the utility of security and other available surveillance solutions.
This architecture involves: automatically monitoring the video of people, vehicles, and other objects, and tracking their behavior within the field of view of the camera. Intelligent video analytics continuously monitor the data provided by these cameras thus ensuring extremely high levels of security and efficiency all around the clock. This level of minute monitoring, 24X7, and without virtually any data outages, involves keeping a track of the huge amount of data, which would otherwise have been humanly impossible to do. The event of overlooking a tiny but important detail due to human error by oversight, fatigue, or slow processing of information, is eliminated.
We can with mounting certainty claim that the way intelligent video analytics is currently developing; will improve the efficiency of the overall retail processes and help to secure different industry verticals. Thus, this will lead to the prevention of shrinkages, better-merchandising management, and optimized facility utilization.
With the proliferation of smart cameras, IoT, sensor, and machine vision-driven technology, organizations can be more adaptive and agile by deploying resources based on insight into where assistance is most required by streamlining manual efforts to accumulate and understand crucial data.
These tasks can be highly monotonous or inefficient for humans to handle – such as monitoring access to a secure room – or even maybe highly dangerous – such as identifying explosives or chemical substances. Other areas, such as an industrial chemical site, may be too risky to monitor in person. Clearly, in many cases, technology can enhance – though not substitute – the efforts of human personnel, who, with access to timely and comprehensive information, can best assess and determine how to respond in various arising situations.
Of course, the impact of Smart Cameras and video analytics is far-reaching even beyond the realm of security. Smart building technology leverages the power of artificial intelligence to increase facility safety, reduce operational costs, improve energy usage, and enhance tenant satisfaction. Video analytics driven by deep learning and computer vision is another emerging area frequently practiced by stakeholders across organizations to complement video surveillance systems and derive insights from video content. Raw video data may contain various insights which are challenging to monitor in real-time or manually analyze post-incident. Sight provides machine-vision-powered object detection, identification, and classification and act a video analysis tools detect the objects and behaviors and index them in metadata to enable quick search and efficient analysis. The process yields structured information by processing unstructured raw video data and empower granular search, smart alerting, and even comprehensive reporting.
Aggregated data extracted from smart cameras are handy for multiple teams and functions within an organization to examine traffic or behavioral trends, benchmark normal activity, and develop strategies for improved productivity.
Moreover, this data on Sight can be converted into dashboard reports and heatmaps to uncover trends in the parking lot, entryway, or cross-campus person or vehicle activity and common navigational patterns. Fortified with key data, decision-makers and their teams can develop contingency plans, prevent future incidents, and traffic bottlenecks, and deploy staff optimally.
These insights also enable operators to configure real-time and role-based triggers to complement pre-defined conditions, behaviors, and prompt responses to evolving situations – whether security risks, developing emergencies, crowd or queue building in a store, QSR, or airport. These capabilities allow organizations and end-users to respond proactively in numerous situations, based on single technology investment.
Video content analytics, among other AI-driven technologies, are one way organizations can enhance their existing technology stack and drive impactful, cross-company effectivity when it comes to safety, security, and efficiency.