{"id":15132102,"url":"https://github.com/utkarsh251106/tracking-with-yolo","last_synced_at":"2026-01-30T05:36:58.874Z","repository":{"id":256247357,"uuid":"854218866","full_name":"Utkarsh251106/Tracking-with-YOLO","owner":"Utkarsh251106","description":"This project uses YOLOv8 and DeepSORT to detect and track children and adults in video streams. It assigns unique IDs, handles re-tracking after occlusions, and outputs an annotated video with labeled bounding boxes.","archived":false,"fork":false,"pushed_at":"2025-01-20T21:45:29.000Z","size":52542,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-05T21:16:40.941Z","etag":null,"topics":["computer-vision","deep-learning","deepsort","machine-learning","numpy","opencv","python","torch","ultralytics","yolov8"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Utkarsh251106.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-09-08T17:30:23.000Z","updated_at":"2025-01-20T21:45:34.000Z","dependencies_parsed_at":null,"dependency_job_id":"9a1f4cfc-0f95-49e2-992d-ddedb2b0b833","html_url":"https://github.com/Utkarsh251106/Tracking-with-YOLO","commit_stats":null,"previous_names":["utkarsh251106/tracking-with-yolo"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Utkarsh251106%2FTracking-with-YOLO","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Utkarsh251106%2FTracking-with-YOLO/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Utkarsh251106%2FTracking-with-YOLO/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Utkarsh251106%2FTracking-with-YOLO/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Utkarsh251106","download_url":"https://codeload.github.com/Utkarsh251106/Tracking-with-YOLO/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247399897,"owners_count":20932881,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["computer-vision","deep-learning","deepsort","machine-learning","numpy","opencv","python","torch","ultralytics","yolov8"],"created_at":"2024-09-26T04:03:40.582Z","updated_at":"2026-01-30T05:36:58.825Z","avatar_url":"https://github.com/Utkarsh251106.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"## Description\nTracking with YOLO is an object detection and tracking project designed to track and count the number of children and adults in a video. The dataset was annotated using Roboflow. For the actual execution of the program, the OpenCV library is used to handle camera and image-related tasks, NumPy is used for various mathematical calculations, and Ultralytics' YOLOv8 is used for object detection. Finally, the Deep SORT algorithm is used for tracking and occlusion prevention purposes. \n\n# How to run it?\n### Step 1: Clone the Repository:\n```bash\ngit clone https://github.com/Utkarsh251106/Tracking-with-YOLO\n```\n### Step 2: Create a conda environment:\n```bash\nconda create -n venv python=3.11.9 -y\nconda activate venv\n```\n\n### Step 3: Install the requirements:\n```bash\npip install -r requirements.txt\n```\n### Step 4: To run the code:\n```bash\n# Start the Jupyter Notebook environment using the command\njupyter notebook\n```\n### Step 5: Run your Code.ipynb file\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Futkarsh251106%2Ftracking-with-yolo","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Futkarsh251106%2Ftracking-with-yolo","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Futkarsh251106%2Ftracking-with-yolo/lists"}