Computer Vision Lab

Nikolay Falaleev

Profile

Nikolay S. Falaleev

London, United Kingdom, nikolasent@gmail.com

Head of AI, Kaggle master

IT Skills and experience

Languages:   Python, C/C++, R, SQL
Tools:   PyTorch, TensorFlow, TFLite, ONNX, OpenCV, FFmpeg, Scikit-learn, NumPy, Docker, TensorRT, OpenVINO, MLflow, DeepStream, Gstreamer, FastAPI
Hardware:   Industrial cameras, Optics, LIDARs, IMU, DL Accelerators
Skills:   Artificial Neural Networks, Deep Learning, Computer Vision, Semantic Segmentation, Object Detection and Tracking, Depth Estimation, Video Event Detection, Sensor Fusion, Calibration, 3D Scene Reconstruction Localization and Pathplanning, Control, Numerical Methods, Optimization, MLOps, Research, Team Leader
Spare-time Projects:   Kaggle competitions, Broadcast Camera Calibration, support of development of argus framework, Real-time CPU person segmentation for privacy in video calls, Image Semantic Segmentation.

Employment

10/2020 - present, Head of AI Sportlight Technology

AI-Driven Analytics for Elite Sports (EPL, SPL, NBA, NHL, and others)

  • Architected and deployed an operational AI system used across all English Premier League (EPL) stadiums, the Saudi Pro League (SPL), and other venues worldwide, achieving industry-leading player tracking accuracy and reducing data QA time and costs by 12x.
  • Defined and implemented the company’s AI strategy, leading cross-functional teams in developing a real-time system that integrates multi-camera video and LIDAR data for insights through multi-modal data fusion (e.g., object detection, tracking, pose estimation, event detection).
  • Spearheaded the transition to real-time data processing, enabling scalability for deployments across various sports and geographies, scaling AI operations by 50x.
  • Ensured adherence to best practices in AI experimentation, software development, and data handling to foster technological excellence and support scalable commercial applications.
  • Positioned the company for future growth by introducing innovations that expanded the AI product roadmap to new markets and sports.

08/2017 - 08/2020, Leading Computer Vision R&D Constanta/OSAI

R&D of applied CV systems for real-time Sports Analytics.

  • Proposed and executed the development of a DL-based system for real-time video analysis of sports events for MR broadcasting, sports analytics, and betting, including hardware design, computer vision R\&D, data management, and scalable deployment.
  • Led the implementation of production-ready inference pipelines and managed project life cycles from initial concept to commercial operation.
  • Pioneered a real-time spatio-temporal video processing ANN architecture, published in a CVPR 2020 paper. The system was showcased in high-profile events such as Tokyo 2020 Olympics, JOOLA North American Teams Championships and National Table Tennis Championship 2020, and has been deployed in numerous venues worldwide for 24/7 commercial operation.
  • Created a Computer Vision module for pool-9 real-time analysis from organizing data collection to product deployment. Video demo, Blog article [rus].

10/2014 - 07/2017 Junior Researcher Academic Research Institute

Constructing stable finite-difference schemes for transient heat conduction in 3D case.

Achievements:

  • 6 scientific papers and 3 conference reports were coauthored.

Education

09/2015 - 07/2017     Master of Science with Honours in Materials Science, Department of Materials Science
      Lomonosov Moscow State University (MSU)
09/2011 - 07/2015     Bachelor of Science in Materials Science, Department of Materials Science
      Lomonosov Moscow State University (MSU)

Continuing education

07/2021 - 08/2021 Oxford Machine Learning Summer School 2021
  Certificate
03/2021 - 08/2021 Intel Edge AI for IoT Developers Nanodegree
  Udacity, Certificate
01/2020 - 06/2020 C++ Nanodegree
  Udacity, Certificate
11/2016 - 10/2017 Self-Driving Car Engineer Nanodegree
  Udacity, Certificate, graduated with the first ever cohort
11/2012 - present >30 MOOCs in Computer and Data Sciences, Robotics, Computer Vision and Machine Learning
  Coursera, Udacity, edX and etc.

Prizes and Awards

2023     SoccerNet Camera Calibration Challenge 2023, CVPR
      Top-1. DL approach to camera calibration from football broadcast videos. The challenges was held at CVPR 2023.
2018     Quick, Draw! Doodle Recognition Challenge, Kaggle
      Top-4% (50/1316). Application of deep neural networks to classification of users drawings into 300+ classes with 50M train samples
2018     TGS Salt Identification Challenge, Kaggle
      Top-0.5% (14/3234). The deep-learning based approach helps to segment seismic images which is crucial for oil and gas company drillers.
2018     Lyft Perception Challenge, Udacity
      Top-3% (4/155) and the fastest neural network pipeline. The solution is based on LinkNet34 for real-time multiclass semantic segmentation.
2018     IEEE’s Signal Processing Society - Camera Model Identification, Kaggle
      Top-3% (15/583). The solution is based on convolutional neural networks as classifiers for camera models captured a given image.
2016     Image-Based Localization Challenge, Udacity
      The 3d place. A deep learning approach based on GoogLeNet inception module was applied for “localization as classification” method for localization of an autonomous vehicle.
2015     ХХVII International Innovation Conference of Young Scientists and Students, Moscow, Russia
      Diploma for the best scientific report “Mathematical modeling and numerical calculations of particles and coatings heating during gas-dynamic spraying”.
2013     Mendeleev Prize for Young Chemists, Kazan, Russia
      The Second Prize for a report: “Properties of single wall carbon nanotubes encapsulated by inorganic compounds”

Patents

  • RU106765 “Device for producing 3D images by a single camera”. A special method for depth map calculation based on an image, produced by the device, was also developed and the gear is able to work as a range finder.

Publications

Deep Learning:

  1. Falaleev N., Chen R. Enhancing Soccer Camera Calibration Through Keypoint Exploitation, // MMSports ‘24: Proceedings of the 7th ACM International Workshop on Multimedia Content Analysis in Sports, 2024, pp. 65-73, DOI: 10.1145/3689061.3689074. GitHub repo.

  2. Cioppa A., Giancola S., …, Falaleev N., et al. SoccerNet 2023 challenges results, // Sports Engineering, V. 27, 2024, DOI: 10.1007/s12283-024-00466-4.

  3. Voeikov R., Falaleev N., Baikulov R. TTNet: Real-time temporal and spatial video analysis of table tennis. // The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2020, pp. 884-885, DOI: 10.1109/CVPRW50498.2020.00450.

Nanotechnology:

  1. Eliseev Andrei A., Kumskov A.S., Falaleev N.S., Zhigalina V.S., Eliseev Artem A., Mitrofanov A.A., Petukhov D.I., Vasiliev A.L., Kisilev N.A. Mass Transport Through Defects in Graphene Layers // Journal of Physical Chemistry C, 2017, DOI: 10.1021/acs.jpcc.7b06100

  2. N. S. Falaleev, A.S. Kumskov, V.G. Zhigalina, I.I. Verbitskiy, A.L. Vasiliev, A. A. Makarova, D. V. Vyalikh, N.A. Kiselev, A.A. Eliseev. Capsulate structure effect on SWNTs doping in RbxAg1−xI@SWNT composites // CrystEngComm, 2017, DOI: 10.1039/c7ce00155j

  3. Andrei A. Eliseev, Nikolay S. Falaleev, Nikolay I. Verbitskiy, Andrei A. Volykhov, Lada V. Yashina, Andrei S. Kumskov, Victoria G. Zhigalina, Alexander L. Vasiliev, Alexey V. Lukashin, Jeremy Sloan, Nikolay A. Kiselev. Size-dependent structure relations between nanotube and encapsulated nanocrystal. // ACS Nano Letters, V. 17 I. 2, 2017, P. 805–810 DOI: 10.1021/acs.nanolett.6b04031

  4. N.A. Kiselev, A.S. Kumskov, V.G. Zhigalina, A.L. Vasiliev, J. Sloan, N.S. Falaleev, N.I. Verbitskiy, A.A. Eliseev. The structure and continuous stoichiometry change of 1DTbBrx@SWCNTs // Journal of Microscopy. V. 262, I. 1, April 2016, P. 92–101 DOI: 10.1111/jmi.12348

  5. Lukashin A.V., Falaleev N.S., Verbitskiy N.I., Volykhov A.A., Verbitskiy I.I., Yashna L.V., Kumskov A.S., Kiselev N.A., Eliseev A.A. Quasi free-standing one-dimensional nanocrystals of PbTe grown in 1.4 nm SWNTs. // Nanosystems: physics, chemistry, mathematics. V. 6, I. 6, 2015, P. 850-856 DOI: 10.17586/2220-8054-2015-6-6-850-856

Google Scholar profile

ResearchGate profile

Public Talks

11/2024     London PyTorch Meetup
      Optimising Video Pipelines for Neural Network Training with PyTorch. Slides, GitHub repo.
11/2024     Computer Vision Summit London 2024
      Transforming Athlete Performance with LIDAR and Video Data. Keynote presentation, 250+ attendees.
11/2023     Computer Vision Summit London 2023
      Deploying unparalleled accuracy in athletes’ performance analysis with LIDAR and video data processing. Keynote presentation, 250+ attendees.
06/2023     CVPR, 9th International Workshop on Computer Vision in Sports
      Presentation on a DL approach to camera calibration. GitHub repo.
07/2022     GSIG Solutions Showcase: Athlete tracking: AI, computer vision, machine learning
      LIDAR and visual data fusion for athlete tracking.

Clubs & Societies

Founded a non-commercial organization OniroAI (Github) for independent research and development in Computer Vision and Artificial Intelligence.

Interests

Self-Driving Cars, Deep Learning, Computer Vision, Robotics, Artificial Intelligence, Mathematical Modeling, UAVs