nvidia-holoscan/holohub
Apr 2026 – Apr 2026
1 Month active
Languages Used
BashPython
Technical Skills
DockerPython developmentobject detectionreal-time systemsvideo processing
ers-brandongevat
PROFILE
Ers-brandongevat
Developed and delivered a TAK Integration App for the nvidia-holoscan/holohub repository, enabling real-time object detection and tracking using YOLOv8 and ByteTrack. The solution supported both V4L2 camera and video replayer inputs, providing local visualization and automated uploads to a TAK server via Cursor-on-Target. Leveraging Docker for reproducible local TAK stack provisioning, the work included first-run scripts and a web UI/proxy for comprehensive end-to-end testing. Automated tests were implemented to validate replayer mode functionality. The project was built primarily with Python and Bash, with additional focus on video processing, secrets management, and thorough documentation updates for maintainability.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
2,089
Activity Months1
Your Network
42 peopleShared Repositories
42
Work History
April 2026
1 Commits • 1 Features
Apr 1, 2026Month: 2026-04 — Delivered a TAK Integration App for holohub, enabling object detection and tracking via YOLOv8 and ByteTrack, with support for V4L2 camera and video replayer inputs. The release includes local visualization, Cursor-on-Target uploads to a TAK server, and a reproducible local TAK stack provisioned via Docker, along with first-run scripts and web UI/proxy. Documentation was updated (README) and automated testing added to exercise the app in replayer mode. Maintenance tasks included secrets handling improvements and codespell ignore updates.
1 Commits • 1 Features
Apr 1, 2026Month: 2026-04 — Delivered a TAK Integration App for holohub, enabling object detection and tracking via YOLOv8 and ByteTrack, with support for V4L2 camera and video replayer inputs. The release includes local visualization, Cursor-on-Target uploads to a TAK server, and a reproducible local TAK stack provisioned via Docker, along with first-run scripts and web UI/proxy. Documentation was updated (README) and automated testing added to exercise the app in replayer mode. Maintenance tasks included secrets handling improvements and codespell ignore updates.
April 2026
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage60.0%
Skills & Technologies
Programming Languages
BashPython
Technical Skills
DockerPython developmentobject detectionreal-time systemsvideo processing
Repositories Contributed To
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