| 1 | |
| 2 | == Installed Camera Information |
| 3 | |
| 4 | **Hikvision DS-2CD5585G0-IZHS** |
| 5 | Mudd floor 1, towards 120th street |
| 6 | Mudd first floor Mech E lab |
| 7 | [https://www.hikvision.com/uploadfile/image/11769_DatasheetofDS2CD5585G0IZ(H)S.pdf] |
| 8 | |
| 9 | **Hikvision DS-2CD5585G0-IZHS** |
| 10 | Mudd 2nd floor, towards Amsterdam balcony camera |
| 11 | |
| 12 | **Hikvision DS-2CD4AC5F-IZH ** |
| 13 | 12MP Outdoor Network Bullet Camera with Night Vision, 2.8-12mm Lens & Built-In Heater S/N: 212528616: |
| 14 | 20191015: installation in Botwinick lab mudd1224, towards 120th street |
| 15 | **Hikvision DS-2CD5585G0-IZHS ** |
| 16 | 2.8mm-12mm (8MP) |
| 17 | Mudd 12thFloor Balcony towards Amsterdam Avenue |
| 18 | |
| 19 | |
| 20 | == COSMOS Cameras Data-set |
| 21 | **1st-floor videos (anonymized*)**: [https://drive.google.com/drive/u/0/folders/1QXrfsLXEKfRfQyc6qzvtg37A0Z1i0io5] |
| 22 | |
| 23 | **2nd-floor videos (anonymized*)**: [https://drive.google.com/drive/u/0/folders/1LR7H4theRazz2_uYHvCFGVVewQmKbWSF] |
| 24 | |
| 25 | **12th-floor videos (120th street)**: [https://drive.google.com/drive/u/0/folders/1SEsocAAIReepdjE4XyVyT4kiqrunv7BU] |
| 26 | |
| 27 | **12th-floor videos (Amsterdam Avenue)**: [https://drive.google.com/drive/u/0/folders/1qC-62s8ohTGg-odyzo7BNw2GDv1OIeoK] |
| 28 | |
| 29 | Videos in this directory are outputs of the COSMOS YOLOv4 blurring pipeline. |
| 30 | |
| 31 | Faces and license plates are anonymized with Gaussian blurred areas defined by bounding box detection coordinates. A high-level overview of the blurring. |
| 32 | |
| 33 | ***Anonymization workflow:** |
| 34 | |
| 35 | 1. Frames are read individually from a video file. |
| 36 | |
| 37 | 2. Each frame is then: |
| 38 | |
| 39 | 2.1 Resized to the input size of the specific YOLOv4 model (960x960 or 1440x1440) |
| 40 | |
| 41 | 2.2 Passed through the YOLOv4 model which outputs bounding box predictions |
| 42 | |
| 43 | 2.3 Blurred corresponding to the bounding box predictions |
| 44 | |
| 45 | 3. Blurred frames are written to the output video file. |
| 46 | |
| 47 | The YOLOv4 blurring model is trained in Darknet on the Mudd 1st floor video |
| 48 | dataset annotated in Summer 2021. Models are converted from Darknet to PyTorch for |
| 49 | integration into the current implementation of the blurring pipeline. |