Subcontractor L3
- End to End perception pipeline creation for entrypoint analytics.
- Used NVIDIA Deepstream on Jetson platform.
- Used Kafka, FastAPI and Flask servers for communications.
- Effectively improved accuracy of detection by atleast 12% per detection.
- Created stablisation pipelines for unstable RTSP and RTMP streams to reduce jitter and lags before feeding the same to Deepstream pipeline.
- Created a Mail and Application based service which would send alerts to concerned entity by mail, whatsapp and in house android application with video of the alerts with the analytical information intact.
- Created a Multi Camera RE-Identification ecosystem which could recognise people over multiple cameras using their vector embeddings.
- Used Triton Inferencing server using docker for providing concurrent inference apis to remote services.
- Use of GAN for increasing the accuracy of pixelated pictures in face detection and pipelines for enhancing the structure.
- Used MLFlow for running model training scripts for production rollouts.
- Technology Stack: Pytorch, Tensorflow and MLFlow for model training.
- Deepstream C++ and Python for implementation.
- OpenCV, Tensorflow for on device deployments using Python and C++.
- FastAPI, Flask for analytics APIs.
- Kafka and Redis for data communication and duplication avoidance.
