Ai-MicroCloud™ for IoT
Driven by plummeting camera prices and the recent successes of computer vision-based video inference applications, organizations are deploying cameras at scale.
The following trends will provide opportunities to video surveillance market participants:
Increased government focus on public safety
Need to utilize and examine unstructured video surveillance data in near real time
Potential significant crime reduction due to surveillance cameras
Growing need among enterprises to leverage Business Intelligence (BI) and actionable insights for advanced operations
Limitations of manual video analysis
Increased use of artificial intelligence and deep learning for video surveillance systems
Video analytics enhances video surveillance systems by performing near real-time event detection tasks, post-event analysis, and extraction of statistical data, while reducing manpower costs and increasing surveillance system effectiveness.
Intrusion systems, integrated with video analytics, offer more accurate automated surveillance. If an intruder triggers a pre-defined security rule, the automated intrusion management system generates an alert, thereby enabling timely and precise responses.
Municipalities must increase surveillance of public places, such as parks, landmarks, bridges, monuments, critical infrastructure to minimize threats to citizens, support law enforcement activities and enhance smart cities management.
Scaling video analytics to massive camera deployments, presents new and mounting challenges, as compute cost grows proportionally to the number of camera feeds. Processing video feeds from large deployments, requires a proportional investment in high-performance compute (HPC) hardware (GPUs) or cloud resources (GPU machine time), the costs of which can easily exceed those of the camera hardware.
Inference accuracy and cost of processing are the two key metrics in video analytics applications. Inference accuracy is a function of the analytics model used and the labeled data, used for training and video characteristics, such as frame resolution and frame rate. These factors also influence the cost of processing – larger models and higher quality videos enable higher accuracy, while requiring the additional cost of increased resource consumption or accepting higher processing latency. When video feeds are analyzed at an Edge or cloud cluster, the cost also includes the bandwidth cost of sending the videos over a network, which increases with the number of video feeds.
AI workload management becomes challenging when video streams from multiple cameras are used by different algorithms to draw inferences simultaneously, e.g., a vehicle tracking algorithm detects a car coming into a gas station and a human action predictive algorithm identifies a person getting out of the vehicle and carrying a suspicious object, like a gun. Pre-processing of video data and Orchestration of compute resources for AI inferences is key to delivering a highly accurate and cost-effective video surveillance solution.
Ai-MicroCloud™ with Video Analytics Algorithms
Zeblok Computational provides a robust technology platform that enables development and implementation of AI-powered video analytics solutions. The solution accommodates video feeds in a variety of formats & resolutions, ranging from a few cameras to thousands of cameras. Zeblok’s Artificial Intelligence/Machine Learning Platform-as-a-Service (AI PaaS) enables data scientists and other AI developers to develop their models using unique curated algorithms from Zeblok’s Intelligence Marketplace and integrate them with enterprise applications easily and seamlessly. Zeblok deploys its AI PaaS to data centers, public clouds and Edge locations, essentially creating a private AI micro cloud for each client.
Service Providers can offer Video-Surveillance-as-a-Service to their clients by developing bespoke AI models and provide sophisticated AI-based Video-Analytics-as-a-Service to a variety of clients responsible for monitoring facilities, such as schools, highways and public spaces to identify vandalism and other suspicious behavior, monitor and analyze traffic patterns, identify traffic violations and many more.
There are 3 main parts in the architecture of this solution.
Video streams from cameras at edge locations
Zeblok AI-PaaS, with relevant AI algorithms in its Intelligence Marketplace
Integration with enterprise applications
Video streams from Cameras at the Edge
Video cameras installed at Edge locations stream data 24/7. AI models need historic datasets for training purposes. Once the model is trained, inferences can be processed at each edge location or at the Core data center. Edge locations must be equipped with appropriate compute, storage, and networking capabilities to handle the volume of data coming in from multiple cameras and to provide inferences at the edge or at the central command center depending on the client applications.
Zeblok AI PaaS
Zeblok Computational’s AI PaaS provides all the composable foundational components that data scientists and other AI developers need, with a simple user interface and familiar frameworks that enables them to get started on model development in minutes, leverage familiar open-source frameworks and original curated AI algorithms, that are ready to be integrated into the model. Zeblok’s powerful orchestration engine enables seamless scalability of HPC and an accelerated data lake. Zeblok AI PaaS can be deployed to any data center on any hardware. Our AI runtime environment provides APIs that can be easily integrated with client applications.
Our proprietary orchestration layer software supports multi-cloud, multi-class scheduling, allowing simple multi-user AI/ML workstations to co-exist with HPC workloads required for substantial AI/ML model training. It permits on-demand usage of GPU capacities for AI/ML model development. It provides a rapid prototyping environment to promote AI models as APIs for rapid consumption into enterprise business processes.
Zeblok, through its affiliation with National Science Foundation’s CVDI, has access to unique AI algorithms in the video analytics space that are available for model development, providing functionality such as:
Early event detection & predictions
Human action recognition
Suspicious object detection
Facial behavior analysis
Suspicious behavior detection
Deep learning for illumination and material analysis
The foundational utilities on the PaaS layer of the Zeblok AI PaaS acts as bridge between the video analytics platform and enterprise applications, such as:
Intrusion Detection: Perimeter protection, boundary monitoring, restricted zone monitoring
Suspicious Activity: Loitering, fighting, vandalism
Traffic & Parking Management: Illegal parking, one-way violation, speeding, no helmet, traffic congestion, vehicle counting
Smoke & Fire Detection: Early warning of smoke and fire in open areas and indoors
Crowd Counting: Crowd counting and flow analysis
Vehicle Identification: License plate detection and recognition
Non-Invasive Body Temperature Monitoring: Passive thermal imaging for temperature monitoring
Dwell Time: Amount of time customer spends in specific areas
Social Distancing and Usage of PPP: Workplace safety & hygiene compliance, managing occupancy, identifying protocol violators
Connect to an existing infrastructure including IP cameras, Analog cameras, NVR’s and DVR’s
Perform preprocessing including data sampling, image, and clip extraction from any connected camera to make AI scalable and cost efficient
Data scientists can begin AI/ML model development in minutes using AI-WorkStation that provides out-of-the-box CUDA optimized AI frameworks with popular data science language bindings, such as R, Scala, and Python.
Cloud Native architecture
Turnkey cloud native AI PaaS provides instant usability and seamless scalability, with flexibility to enable additional services
Intelligence Marketplace for Algorithms
Growing library of proven original AI algorithms available within AI-WorkStation. Curated via closed loop validation ecosystem, enabling customized AI model development to suit any requirement.
Flexibility to deploy composable foundational components of Zeblok AI PaaS within the MSSP’s data center or within third party cloud service providers like AWS, GCP, Azure or IBM
Scalable & Optimized for Video Analytics
An integrated AI PaaS that can scale from few cameras to thousands of cameras located across multiple geographical locations to build AI models to suit any need - near real-time, periodic, or on-demand video analytics
Turnkey High Performance Computing Orchestration
Deploy AI workloads to single or hundreds of Nvidia GPUs with a single click making it easy to work on large volume of datasets for AI model development
Agile Edge deployment
Hardware agnostic autonomous provisioning of edge infrastructure with flexibility to push AI inferences to any Edge location provides agility to provision single or thousands of Edge locations globally
Zeblok enables development of bespoke Video Surveillance-as-a-Service (VSaaS) or Video Analytics-as-a-Service (VAaaS) that offer advanced surveillance capabilities through automated, continuous and accurate monitoring. They are cost-effective and indispensable tools for operators in multiple industries who are confronted with the massive quantity of video footage streamed from many cameras.
Public Safety/Enterprise Security/Industrial Security:
Detect perimeter intrusions in near real-time, with minimal false alarms
Generate forensic evidence for use as within investigations
Comply with law enforcement agencies’ response requirements
Protect high value assets, infrastructure
Detect hazards at transportation hubs, such as airports, seaports, rail and bus terminals
Detect loitering, crowd formations indicative of possible criminal activity or safety issues
Analyze traffic patterns, detect traffic violations and possible road safety hazards
Detect stopped vehicles or obstacles
Count vehicles, identify unsafe driving
• Detect unauthorized campus perimeter breaches
• Detect guns or other hazardous objects and raise alarms
• Detect and report suspicious behavior, such as fighting or vandalism
• Generate student and/or vehicle counts and traffic patterns within the campus
• Count patrons entering/leaving to determine occupancy
• Identify high-density areas to optimize positioning of promotional displays
• Detect and report suspicious behavior, such as fighting or vandalism
• Identify areas that do not attract customer traffic to optimize layout
• Detect shoplifting, other suspicious behavior
• Monitor cash register efficiency to reduce queue time and improve customer satisfaction