Update README.md

README.md

@@ -1,26 +1,27 @@
 # Physical AI Smart Spaces Dataset
 
 ## Overview
+Comprehensive, annotated dataset for multi-camera tracking and 2D/3D object detection. This dataset is synthetically generated with Omniverse.
 
-This comprehensive synthetic dataset, generated using the NVIDIA Omniverse Platform, supports advanced research in multi-target multi-camera (MTMC) tracking and 2D/3D object detection tasks. It includes synchronized videos from multiple cameras capturing diverse indoor environments, such as warehouses, hospitals, retail stores, and laboratories. Privacy is ensured, as the dataset contains no personal data.
+This dataset consists of over 250 hours of video from across nearly 1,500 cameras from indoor scenes in warehouses, hospitals, retail, and more. The dataset is time synchronized for tracking humans across multiple cameras using feature representation and no personal data.
 
-The dataset has been officially adopted by the AI City Challenge:
-- **MTMC_Tracking_2024**: Utilized at the 8th AI City Challenge Workshop at CVPR 2024.
-- **MTMC_Tracking_2025**: Scheduled for the 9th AI City Challenge in 2025.
+## Dataset Description
 
-## Key Features of MTMC_Tracking_2025
-- Inclusion of **3D Bounding Boxes**.
-- Comprehensive **Calibration Data** (camera locations, orientations, intrinsic and extrinsic matrices).
-- Multiple object classes beyond persons, including humanoids and autonomous mobile robots.
+### Dataset Owner(s)
+NVIDIA
 
-## Dataset Quantification
+### Dataset Creation Date
+We started to create this dataset in December, 2023. First version was completed and released as part of 8th AI City Challenge in conjunction with CVPR 2024.
 
-| Dataset                 | Annotation Type                                       | Hours | Cameras | Object Classes & Counts                                       | No. 3D Boxes | No. 2D Boxes | Total Size |
-|-------------------------|-------------------------------------------------|-------------|----------------|-------------------------------------------------------------|--------------|--------------|------------|
-| **MTMC_Tracking_2024** | 2D bounding boxes, multi-camera tracking IDs | 212 | 953 | Person: 2,481 | N/A | 135M | 198 GB |
-| **MTMC_Tracking_2025**<br>(Train & Validation only) | 2D & 3D bounding boxes, multi-camera tracking IDs | 42 | 504 | Person: 292<br>Forklift: 13<br>NovaCarter: 28<br>Transporter: 23<br>FourierGR1T2: 6<br>AgilityDigit: 1<br>**Overall:** 363 | 8.9M | 73M | 74 GB |
 
-## Dataset Structure
+### Dataset Characterization
+- Data Collection Method: Synthetic
+- Labeling Method: Automatic with IsaacSim
+
+### Video Format
+- Video Standard: MP4 (H.264)
+- Video Resolution: 1080p
+- Video Frame rate: 30 FPS
 
 ### Ground Truth Format (MOTChallenge) for `MTMC_Tracking_2024`
 Annotations are provided in the following text format per line:
@@ -38,7 +39,7 @@ Annotations are provided in the following text format per line:
 The video file and calibration (camera matrix and homography) are provided for each camera view.
 
 ### Directory Structure for `MTMC_Tracking_2025`
-- `videos/`: MP4 files, 1920×1080 pixels resolution at 30 FPS.
+- `videos/`: Video files.
 - `ground_truth.json`: Detailed ground truth annotations (see below).
 - `calibration.json`: Camera calibration and metadata.
 - `map.png`: Visualization map in top-down view.
@@ -92,11 +93,18 @@ Contains detailed calibration metadata per sensor:
 }
 ```
 
-## Evaluation
+### Evaluation
 
 - **2024 Edition**: Evaluation based on HOTA scores at the [2024 AI City Challenge Server](https://eval.aicitychallenge.org/aicity2024). The submission is currently disabled, as the ground truths of test set are provided with this release.
 - **2025 Edition**: Evaluation system and test set forthcoming in the 2025 AI City Challenge.
 
+## Dataset Quantification
+
+| Dataset                 | Annotation Type                                       | Hours | Cameras | Object Classes & Counts                                       | No. 3D Boxes | No. 2D Boxes | Total Size |
+|-------------------------|-------------------------------------------------|-------------|----------------|-------------------------------------------------------------|--------------|--------------|------------|
+| **MTMC_Tracking_2024** | 2D bounding boxes, multi-camera tracking IDs | 212 | 953 | Person: 2,481 | N/A | 135M | 198 GB |
+| **MTMC_Tracking_2025**<br>(Train & Validation only) | 2D & 3D bounding boxes, multi-camera tracking IDs | 42 | 504 | Person: 292<br>Forklift: 13<br>NovaCarter: 28<br>Transporter: 23<br>FourierGR1T2: 6<br>AgilityDigit: 1<br>**Overall:** 363 | 8.9M | 73M | 74 GB |
+
 ## References
 
 Please cite the following papers when using this dataset:
@@ -118,9 +126,7 @@ year = {2024}
 }
 ```
 
-## Ethical Considerations
-
+## Ethical Considerations:
 NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications.  When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.   
 
-
 Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).