Horizontal scaling featured image
Video is a common data input in the field of Computer Vision & Image processing. For example, in applications such as infrastructure maintenance and defect detection, video is captured from a camera source mounted on a drone or a locomotive.

Horizontal scaling of Video processing applications

1. Introduction

Video is a common data input in the field of Computer Vision & Image processing. For example, in applications such as infrastructure maintenance and defect detection, video is captured from a camera source mounted on a drone or a locomotive. The captured data passes through a software pipeline where numerous operations such as image transformations, ML (Machine Learning) inference and tracking are performed.

Video processing can be seen as a sequence of operations done repeatedly for each frame. Such a serial process is easy to implement, but not practical in most cases due to low performance. To make the processing faster and to keep the accuracy high, the optimum solution is to come up with a procedure that will allow the processing steps to run simultaneously. Here, high volume video data is taken and split into a number of smaller videos called chunks and processed in parallel.

2. Celery task queue

Celery is a distributed task queue with focus on real-time processing and task scheduling. Celery systems can consist of multiple workers and brokers, which leads to horizontal scaling and high availability. Using the in-built ‘pool’ option, Celery tasks can be run asynchronously.

Documentation on Celery workers can be found here.

You can read more on Celery in the following 2 Ignitarium blogs:

  1. Use of Celery in Serverless systems for scaling AI workloads
  2. A Hands-on Guide to Backend Performance Optimization of a SaaS platform for scalable AI workloads.

In the subsequent sections, we’ll describe the use of Celery to manage the processing of video chunks.

3. Chunk processing

Before deep diving, let’s have clarity on chunks, and why we need it. After all, video is a sequence of frames captured and displayed at a given frame rate (FPS). Suppose we have enough compute servers in a cluster and our pipeline is set up for running inference, one simple method is to capture images, send them to each server asynchronously and wait for results. What are we missing here? There might be a relation between the current frame and a previous frame or a set of previous frames. Sending each image to servers individually means we are losing this valuable relation. For example, for tracking applications, it’s important to rely upon previous inference results to take a decision on current inference. This is exactly why we need chunk processing.

A chunk is a minimal representation of the original video. It should have all the properties of the original input video, ensuring that the previous information is not lost while calling the inference pipeline.

Fig 1:  Illustration of dividing a video into a number of chunks

In the example shown in Fig 1., an input video of 90 frames is split into 3 chunks. A fixed chunk size of 30 is selected with 1 overlapping frame across chunks. Note that the first chunk will not have an initial overlapping frame. Similarly, the last chunk has 32 frames and not the ‘fixed’ chunk size of 30 frames; this is because there are no more chunks after this one. The last chunk will have a size equal to the defined fixed chunk size plus the remaining frames for that video.

Fig 2: Flow Diagram

Fig 2. describes the overall workflow, where the input video is split into “n” number of chunks, each having a fixed chunk size.

W1, W2, W3, …. Wn represents different Celery workers that process these chunks.

The main components in the pipeline are:

  • Broker:

    • Used for storing tasks. The default Celery broker is RabbitMQ

  • Backend:

    • Stores results of task executions. Preferred Celery backend is Redis

  • Worker:

    • Process that runs on CPU or GPU and executes task from the task queue

3.1 Concurrency factor

Concurrency refers to the number of worker processes/threads. By default, multiprocessing is used to perform concurrent execution of tasks and concurrency defaults to the number of CPUs available on the machine.

  • Concurrency can be set to high (4-8) for a relatively faster server.
  • Concurrency should be lower (~1) for relatively slower servers.
  • These worker configurations can be done at the time of worker bring-up.

3.2 Network File System (NFS)

Fig 3: NFS server-client, Source

  • NFS is a distributed file system protocol which allows you to mount remote directories on a server.
  • Storage space is managed in a central location with writes to this central storage occurring from multiple clients.

All the machines in a cluster will use NFS. The input video to be processed will be stored on the shared NFS disk and will be accessible to all servers in the cluster. Chunks from this input video will be processed by workers within the cluster with temporary chunk results being written to the NFS-mounted shared space. For example, Chunk1 might be processed by Worker 1 (running on physical server 1) while Chunk 4 might be processed by Worker 4 (running on physical server 4).

3.3 Sequential Block

The Sequential block (Fig 2.) takes the results (video and other tracking results) from each chunk and generates a combined results video. This sequential block should not become a bottleneck in the entire pipeline. So, all the computationally expensive processes are performed by the pool of workers. The Sequential block just waits for the results to be generated by the worker pool and uses their references as input and then combines them sequentially to generate a single video. Per frame annotations on a global scale (w.r.t full video) will be stored in a dictionary (global dictionary).

3.4 Overlay Block

The Overlay module operates on chunks; for each chunk, this module will set the correct index to the global dictionary in order to extract the relevant annotations for a particular frame. The extracted information (eg. unique tile id on a rail track) will be overlayed on the video in such a manner that allows playback of either the entire video with all results annotated or only selected sections of the input video with its corresponding annotations.

4. Analysis

Table 1: Analysis of running the same video with different cluster sizes & different chunks.

Table 1 describes the observations while running the pipeline on a full-length video (1800 frames) file.

  • Each worker has been configured with custom concurrency values
  • Adding more workers to the cluster improves overall FPS
  • Increasing the number of chunks improves overall FPS
  • Setting low concurrency to the slow speed workers improved overall FPS

5. Summary

Designing video processing pipelines at scale requires careful planning and proper testing. Instead of processing an entire video sequentially, chunk processing has significant benefits. Queue-based asynchronous methods like Celery is one of the best choices to consider for such use cases. Celery provides advanced features like autoscale, which can be leveraged to get maximum throughput and stability. This article described chunk based parallel processing of a single input video. This method of chunk processing is of even greater relevance when multiple input videos of large frame count are to be processed maintaining high processing throughput.

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Human Pose Detection & Classification

Some Buildings in a city

Features:

  • Suitable for real time detection on edge devices
  • Detects human pose / key points and recognizes movement / behavior
  • Light weight deep learning models with good accuracy and performance

Target Markets:

  • Patient Monitoring in Hospitals
  • Surveillance
  • Sports/Exercise Pose Estimation
  • Retail Analytics

OCR / Pattern Recognition

Some Buildings in a city

Use cases :

  • Analog dial reading
  • Digital meter reading
  • Label recognition
  • Document OCR

Highlights :

  • Configurable for text or pattern recognition
  • Simultaneous Analog and Digital Dial reading
  • Lightweight implementation

Behavior Monitoring

Some Buildings in a city

Use cases :

  • Fall Detection
  • Social Distancing

Highlights :

  • Can define region of interest to monitor
  • Multi-subject monitoring
  • Multi-camera monitoring
  • Alarm triggers

Attire & PPE Detection

Some Buildings in a city

Use cases :

  • PPE Checks
  • Disallowed attire checks

Use cases :

  • Non-intrusive adherence checks
  • Customizable attire checks
  • Post-deployment trainable

 

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    Real Time Color Detection​

    Use cases :

    • Machine vision applications such as color sorter or food defect detection

    Highlights :

    • Color detection algorithm with real time performance
    • Detects as close to human vison as possible including color shade discrimination
    • GPGPU based algorithm on NVIDIA CUDA and Snapdragon Adreno GPU
    • Extremely low latency (a few 10s of milliseconds) for detection
    • Portable onto different hardware platforms

    Missing Artifact Detection

    Use cases :

    • Detection of missing components during various stages of manufacturing of industrial parts
    • Examples include : missing nuts and bolts, missing ridges, missing grooves on plastic and metal blocks

    Highlights :

    • Custom neural network and algorithms to achieve high accuracy and inference speed
    • Single-pass detection of many categories of missing artifacts
    • In-field trainable neural networks with dynamic addition of new artifact categories
    • Implementation using low cost cameras and not expensive machine-vision cameras
    • Learning via the use of minimal training sets
    • Options to implement the neural network on GPU or CPU based systems

    Real Time Manufacturing Line Inspection

    Use cases :

    • Detection of defects on the surface of manufactured goods (metal, plastic, glass, food, etc.)
    • Can be integrated into the overall automated QA infrastructure on an assembly line.

    Highlights :

    • Custom neural network and algorithms to achieve high accuracy and inference speed
    • Use of consumer or industrial grade cameras
    • Requires only a few hundred images during the training phase
    • Supports incremental training of the neural network with data augmentation
    • Allows implementation on low cost GPU or CPU based platforms

    Ground Based Infrastructure analytics

    Some Buildings in a city

    Use cases :

    • Rail tracks (public transport, mining, etc.)
    • Highways
    • Tunnels

    Highlights :

    • Analysis of video and images from 2D & 3D RGB camera sensors
    • Multi sensor support (X-ray, thermal, radar, etc.)
    • Detection of anomalies in peripheral areas of core infrastructure (Ex: vegetation or stones near rail tracks)

    Aerial Analytics

    Use cases :

    • Rail track defect detection
    • Tower defect detection: Structural analysis of Power
      transmission towers
    • infrastructure mapping

    Highlights :

    • Defect detection from a distance
    • Non-intrusive
    • Automatic video capture with perfectly centered ROI
    • No manual intervention is required by a pilot for
      camera positioning

    SANJAY JAYAKUMAR

    Co-founder & CEO

     

    Founder and Managing director of Ignitarium, Sanjay has been responsible for defining Ignitarium’s core values, which encompass the organisation’s approach towards clients, partners, and all internal stakeholders, and in establishing an innovation and value-driven organisational culture.

     

    Prior to founding Ignitarium in 2012, Sanjay spent the initial 22 years of his career with the VLSI and Systems Business unit at Wipro Technologies. In his formative years, Sanjay worked in diverse engineering roles in Electronic hardware design, ASIC design, and custom library development. Sanjay later handled a flagship – multi-million dollar, 600-engineer strong – Semiconductor & Embedded account owning complete Delivery and Business responsibility.

     

    Sanjay graduated in Electronics and Communication Engineering from College of Engineering, Trivandrum, and has a Postgraduate degree in Microelectronics from BITS Pilani.

     

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      RAMESH EMANI

      Board Member

      Ramesh was the Founder and CEO of Insta Health Solutions, a software products company focused on providing complete hospital and clinic management solutions for hospitals and clinics in India, the Middle East, Southeast Asia, and Africa. He raised Series A funds from Inventus Capital and then subsequently sold the company to Practo Technologies, India. Post-sale, he held the role of SVP and Head of the Insta BU for 4 years. He has now retired from full-time employment and is working as a consultant and board member.

       

      Prior to Insta, Ramesh had a 25-year-long career at Wipro Technologies where he was the President of the $1B Telecom and Product Engineering Solutions business heading a team of 19,000 people with a truly global operations footprint. Among his other key roles at Wipro, he was a member of Wipro's Corporate Executive Council and was Chief Technology Officer.

       

      Ramesh is also an Independent Board Member of eMIDs Technologies, a $100M IT services company focused on the healthcare vertical with market presence in the US and India.

       

      Ramesh holds an M-Tech in Computer Science from IIT-Kanpur.

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      MALAVIKA GARIMELLA​

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      A professional with a 14-year track record in technology marketing, Malavika heads marketing in Ignitarium. Responsible for all branding, positioning and promotional initiatives in the company, she has collaborated with technical and business teams to further strengthen Ignitarium's positioning as a key E R&D services player in the ecosystem.

      Prior to Ignitarium, Malavika has worked in with multiple global tech startups and IT consulting companies as a marketing consultant. Earlier, she headed marketing for the Semiconductor & Systems BU at Wipro Technologies and worked at IBM in their application software division.

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      Pradeep comes with an overall experience of 26 years across IT services and Academia. In his previous role at Virtusa, he played the role of Delivery Leader for the Middle East geography. He has handled complex delivery projects including the transition of large engagements, account management, and setting up new delivery centers.

      Pradeep graduated in Industrial Engineering and Management, went on to secure an MBA from CUSAT, and cleared UGN Net in Management. He also had teaching stints at his alma mater, CUSAT, and other management institutes like DCSMAT. A certified P3O (Portfolio, Program & Project Management) from the Office of Government Commerce, UK, Pradeep has been recognized for key contributions in the Management domain, at his previous organizations, Wipro & Virtusa.

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      Ashwin strongly believes in the customer-first approach and works to add value and enhance the experiences of our customers.

       

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      AZIF SALY

      Vice President – Sales & Business Development

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      Azif handled key accounts and sales process initiatives at Sankalp Semiconductors. Azif has pursued entrepreneurial interests in the past and was associated with multiple start-ups in various executive roles. His start-up was successful in raising seed funds from Nokia, India. During his tenure at Nokia, he played a key role in driving product evangelism and customer success functions for the multimedia division.

       

      At Wipro, he was involved in customer engagement with global customers in APAC and US.

       

      RAJU KUNNATH Vice President – Enterprise & Mobility

      RAJU KUNNATH

      Distinguished Engineer – Digital

      At Ignitarium, Raju's charter is to architect world class Digital solutions at the confluence of Edge, Cloud and Analytics. Raju has over 25 years of experience in the field of Telecom, Mobility and Cloud. Prior to Ignitarium, he worked at Nokia India Pvt. Ltd. and Sasken Communication Technologies in various leadership positions and was responsible for the delivery of various developer platforms and products.

       

      Raju graduated in Electronics Engineering from Model Engineering College, Cochin and has an Executive Post Graduate Program (EPGP) in Strategy and Finance from IIM Kozhikode.

       

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      PRADEEP SUKUMARAN

      Vice President - Software Engineering

      Pradeep heads the Software Engineering division, with a charter to build and grow a world-beating delivery team. He is responsible for all the software functions, which includes embedded & automotive software, multimedia, and AI & Digital services

      At Ignitarium, he was previously part of the sales and marketing team with a special focus on generating a sales pipeline for Vision Intelligence products and services, working with worldwide field sales & partner ecosystems in the U.S  Europe, and APAC.

      Prior to joining Ignitarium in 2017, Pradeep was Senior Solutions Architect at Open-Silicon, an ASIC design house. At Open-Silicon, where he spent a good five years, Pradeep was responsible for Front-end, FPGA, and embedded SW business development, marketing & technical sales and also drove the IoT R&D roadmap. Pradeep started his professional career in 2000 at Sasken, where he worked for 11 years, primarily as an embedded multimedia expert, and then went on to lead the Multimedia software IP team.

      Pradeep is a graduate in Electronics & Communication from RVCE, Bangalore.

       

      SUJEET SREENIVASAN Vice President – Embedded

      SUJEET SREENIVASAN

      Vice President – Automotive Technology

       

      Sujeet is responsible for driving innovation in Automotive software, identifying Automotive technology trends and advancements, evaluating their potential impact, and development of solutions to meet the needs of our Automotive customers.

      At Ignitarium, he was previously responsible for the growth and P&L of the Embedded Business unit focusing on Multimedia, Automotive, and Platform software.

      Prior to joining Ignitarium in 2016, Sujeet has had a career spanning more than 16 years at Wipro. During this stint, he has played diverse roles from Solution Architect to Presales Lead covering various domains. His technical expertise lies in the areas of Telecom, Embedded Systems, Wireless, Networking, SoC modeling, and Automotive. He has been honored as a Distinguished Member of the Technical Staff at Wipro and has multiple patents granted in the areas of Networking and IoT Security.

      Sujeet holds a degree in Computer Science from Government Engineering College, Thrissur.

       

      RAJIN RAVIMONY Distinguished Engineer

      RAJIN RAVIMONY

      Distinguished Engineer

       

      At Ignitarium, Rajin plays the role of Distinguished Engineer for complex SoCs and systems. He's an expert in ARM-based designs having architected more than a dozen SoCs and played hands-on design roles in several tens more. His core areas of specialization include security and functional safety architecture (IEC61508 and ISO26262) of automotive systems, RTL implementation of math intensive signal processing blocks as well as design of video processing and related multimedia blocks.

       

      Prior to Ignitarium, Rajin worked at Wipro Technologies for 14 years where he held roles of architect and consultant for several VLSI designs in the automotive and consumer domains.

       

      Rajin holds an MS in Micro-electronics from BITS Pilani.

       

      SIBY ABRAHAM Executive Vice President, Strategy

      SIBY ABRAHAM

      Executive Vice President, Strategy

       

      As EVP, of Strategy at Ignitarium, Siby anchors multiple functions spanning investor community relations, business growth, technology initiatives as well and operational excellence.

       

      Siby has over 31 years of experience in the semiconductor industry. In his last role at Wipro Technologies, he headed the Semiconductor Industry Practice Group where he was responsible for business growth and engineering delivery for all of Wipro’s semiconductor customers. Prior to that, he held a vast array of crucial roles at Wipro including Chief Technologist & Vice President, CTO Office, Global Delivery Head for Product Engineering Services, Business Head of Semiconductor & Consumer Electronics, and Head of Unified Competency Framework. He was instrumental in growing Wipro’s semiconductor business to over $100 million within 5 years and turning around its Consumer Electronics business in less than 2 years. In addition, he was the Engineering Manager for Enthink Inc., a semiconductor IP-focused subsidiary of Wipro. Prior to that, Siby was the Technical Lead for several of the most prestigious system engineering projects executed by Wipro R&D.

       

      Siby has held a host of deeply impactful positions, which included representing Wipro in various World Economic Forum working groups on Industrial IOT and as a member of IEEE’s IOT Steering Committee.

       

      He completed his MTech. in Electrical Engineering (Information and Control) from IIT, Kanpur and his BTech. from NIT, Calicut

       

      SUJEETH JOSEPH Chief Product Officer

      SUJEETH JOSEPH

      Chief Technology Officer

       

      As CTO, Sujeeth is responsible for defining the technology roadmap, driving IP & solution development, and transitioning these technology components into practically deployable product engineering use cases.

       

      With a career spanning over 30+ years, Sujeeth Joseph is a semiconductor industry veteran in the SoC, System and Product architecture space. At SanDisk India, he was Director of Architecture for the USD $2B Removable Products Group. Simultaneously, he also headed the SanDisk India Patenting function, the Retail Competitive Analysis Group and drove academic research programs with premier Indian academic Institutes. Prior to SanDisk, he was Chief Architect of the Semiconductor & Systems BU (SnS) of Wipro Technologies. Over a 19-year career at Wipro, he has played hands-on and leadership roles across all phases of the ASIC and System design flow.

       

      He graduated in Electronics Engineering from Bombay University in 1991.

       

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      Co-founder & COO

       

      As Ignitarium's Co-founder and COO, Sujith is responsible for driving the operational efficiency and streamlining process across the organization. He is also responsible for the growth and P&L of the Semiconductor Business Unit.

       

      Apart from establishing a compelling story in VLSI, Sujith was responsible for Ignitarium's foray into nascent technology areas like AI, ML, Computer Vision, and IoT, nurturing them in our R&D Lab - "The Crucible".

       

      Prior to founding Ignitarium, Sujith played the role of a VLSI architect at Wipro Technologies for 13 years. In true hands-on mode, he has built ASICs and FPGAs for the Multimedia, Telecommunication, and Healthcare domains and has provided technical leadership for many flagship projects executed by Wipro.

       

      Sujith graduated from NIT - Calicut in the year 2000 in Electronics and Communications Engineering and thereafter he has successfully completed a one-year executive program in Business Management from IIM Calcutta.

       

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      RAMESH SHANMUGHAM

      Co-founder & CRO

      As Co-founder and Chief Revenue Officer of Ignitarium, Ramesh has been responsible for global business and marketing as well as building trusted customer relationships upholding the company's core values.

      Ramesh has over 25 years of experience in the Semiconductor Industry covering all aspects of IC design. Prior to Ignitarium, Ramesh was a key member of the senior management team of the semiconductor division at Wipro Technologies. Ramesh has played key roles in Semiconductor Delivery and Pre-sales at a global level.

      Ramesh graduated in Electronics Engineering from Model Engineering College, Cochin, and has a Postgraduate degree in Microelectronics from BITS Pilani.