| --- |
| license: fair-noncommercial-research-license |
| configs: |
| - config_name: mot |
| data_files: |
| - split: train |
| path: mot/train-* |
| - config_name: obj_det_seg |
| data_files: |
| - split: acs_ec |
| path: obj_det_seg/acs_ec-* |
| - split: acs_eg |
| path: obj_det_seg/acs_eg-* |
| - split: ie_central |
| path: obj_det_seg/ie_central-* |
| - split: r_central |
| path: obj_det_seg/r_central-* |
| default: true |
| - config_name: obj_det_seg_efficient_grounded_sam |
| data_files: |
| - split: acs_ec |
| path: obj_det_seg_efficient_grounded_sam/acs_ec-* |
| - split: acs_eg |
| path: obj_det_seg_efficient_grounded_sam/acs_eg-* |
| - split: ie_central |
| path: obj_det_seg_efficient_grounded_sam/ie_central-* |
| - split: r_central |
| path: obj_det_seg_efficient_grounded_sam/r_central-* |
| - config_name: obj_det_seg_grounded_sam |
| data_files: |
| - split: acs_ec |
| path: obj_det_seg_grounded_sam/acs_ec-* |
| - split: acs_eg |
| path: obj_det_seg_grounded_sam/acs_eg-* |
| - split: ie_central |
| path: obj_det_seg_grounded_sam/ie_central-* |
| - split: r_central |
| path: obj_det_seg_grounded_sam/r_central-* |
| - config_name: sam3 |
| data_files: |
| - split: train |
| path: sam3/train-* |
| - config_name: tracking |
| data_files: |
| - split: acs_ec |
| path: tracking/acs_ec-* |
| - split: acs_eg |
| path: tracking/acs_eg-* |
| - split: ie_central |
| path: tracking/ie_central-* |
| - split: r_central |
| path: tracking/r_central-* |
| dataset_info: |
| - config_name: mot |
| features: |
| - name: image |
| dtype: image |
| - name: type |
| dtype: string |
| - name: scene |
| dtype: string |
| - name: objects |
| struct: |
| - name: bbox |
| list: |
| list: float32 |
| length: 4 |
| - name: category |
| list: |
| class_label: |
| names: |
| '0': person |
| - name: area |
| list: float32 |
| - name: id |
| list: int64 |
| - name: score |
| list: float32 |
| - name: track_id |
| list: int64 |
| - name: image_id |
| dtype: int64 |
| - name: width |
| dtype: int32 |
| - name: height |
| dtype: int32 |
| - name: recording_id |
| dtype: string |
| - name: file_name |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 317178253 |
| num_examples: 1305 |
| download_size: 309416593 |
| dataset_size: 317178253 |
| - config_name: obj_det_seg |
| features: |
| - name: image |
| dtype: image |
| - name: type |
| dtype: string |
| - name: label_source |
| dtype: string |
| - name: scene |
| dtype: string |
| - name: objects |
| struct: |
| - name: bbox |
| list: |
| list: float32 |
| length: 4 |
| - name: category |
| list: |
| class_label: |
| names: |
| '0': person |
| - name: area |
| list: float32 |
| - name: id |
| list: int64 |
| - name: score |
| list: float32 |
| - name: track_id |
| list: int64 |
| - name: image_id |
| dtype: int64 |
| - name: width |
| dtype: int32 |
| - name: height |
| dtype: int32 |
| - name: recording_id |
| dtype: string |
| - name: file_name |
| dtype: string |
| splits: |
| - name: acs_ec |
| num_bytes: 643471147 |
| num_examples: 2802 |
| - name: acs_eg |
| num_bytes: 630798025 |
| num_examples: 3975 |
| - name: ie_central |
| num_bytes: 452418319 |
| num_examples: 1650 |
| - name: r_central |
| num_bytes: 83111291 |
| num_examples: 380 |
| download_size: 1843335951 |
| dataset_size: 1809798782 |
| - config_name: obj_det_seg_efficient_grounded_sam |
| features: |
| - name: image |
| dtype: image |
| - name: type |
| dtype: string |
| - name: label_source |
| dtype: string |
| - name: scene |
| dtype: string |
| - name: objects |
| struct: |
| - name: bbox |
| list: |
| list: float32 |
| length: 4 |
| - name: category |
| list: |
| class_label: |
| names: |
| '0': person |
| - name: area |
| list: float32 |
| - name: id |
| list: int64 |
| - name: score |
| list: float32 |
| - name: track_id |
| list: int64 |
| - name: image_id |
| dtype: int64 |
| - name: width |
| dtype: int32 |
| - name: height |
| dtype: int32 |
| - name: recording_id |
| dtype: string |
| - name: file_name |
| dtype: string |
| splits: |
| - name: acs_ec |
| num_bytes: 640472191 |
| num_examples: 2802 |
| - name: acs_eg |
| num_bytes: 630117991 |
| num_examples: 3975 |
| - name: ie_central |
| num_bytes: 452026243 |
| num_examples: 1650 |
| - name: r_central |
| num_bytes: 83092451 |
| num_examples: 380 |
| download_size: 1838936390 |
| dataset_size: 1805708876 |
| - config_name: obj_det_seg_grounded_sam |
| features: |
| - name: image |
| dtype: image |
| - name: type |
| dtype: string |
| - name: label_source |
| dtype: string |
| - name: scene |
| dtype: string |
| - name: objects |
| struct: |
| - name: bbox |
| list: |
| list: float32 |
| length: 4 |
| - name: category |
| list: |
| class_label: |
| names: |
| '0': person |
| - name: area |
| list: float32 |
| - name: id |
| list: int64 |
| - name: score |
| list: float32 |
| - name: track_id |
| list: int64 |
| - name: image_id |
| dtype: int64 |
| - name: width |
| dtype: int32 |
| - name: height |
| dtype: int32 |
| - name: recording_id |
| dtype: string |
| - name: file_name |
| dtype: string |
| splits: |
| - name: acs_ec |
| num_bytes: 640444171 |
| num_examples: 2802 |
| - name: acs_eg |
| num_bytes: 630078241 |
| num_examples: 3975 |
| - name: ie_central |
| num_bytes: 452009743 |
| num_examples: 1650 |
| - name: r_central |
| num_bytes: 83088651 |
| num_examples: 380 |
| download_size: 1838935198 |
| dataset_size: 1805620806 |
| - config_name: sam3 |
| features: |
| - name: image |
| dtype: image |
| - name: type |
| dtype: string |
| - name: scene |
| dtype: string |
| - name: objects |
| struct: |
| - name: bbox |
| list: |
| list: float32 |
| length: 4 |
| - name: category |
| list: |
| class_label: |
| names: |
| '0': person |
| - name: area |
| list: float32 |
| - name: id |
| list: int64 |
| - name: score |
| list: float32 |
| - name: track_id |
| list: int64 |
| - name: image_id |
| dtype: int64 |
| - name: width |
| dtype: int32 |
| - name: height |
| dtype: int32 |
| - name: recording_id |
| dtype: string |
| - name: file_name |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 2016686472 |
| num_examples: 8807 |
| download_size: 1843335662 |
| dataset_size: 2016686472 |
| - config_name: tracking |
| features: |
| - name: image |
| dtype: image |
| - name: type |
| dtype: string |
| - name: scene |
| dtype: string |
| - name: objects |
| struct: |
| - name: bbox |
| list: |
| list: float32 |
| length: 4 |
| - name: category |
| list: |
| class_label: |
| names: |
| '0': person |
| - name: area |
| list: float32 |
| - name: id |
| list: int64 |
| - name: score |
| list: float32 |
| - name: track_id |
| list: int64 |
| - name: image_id |
| dtype: int64 |
| - name: width |
| dtype: int32 |
| - name: height |
| dtype: int32 |
| - name: recording_id |
| dtype: string |
| - name: file_name |
| dtype: string |
| splits: |
| - name: acs_ec |
| num_bytes: 42616582 |
| num_examples: 174 |
| - name: acs_eg |
| num_bytes: 34449048 |
| num_examples: 215 |
| - name: ie_central |
| num_bytes: 149362435 |
| num_examples: 536 |
| - name: r_central |
| num_bytes: 83078500 |
| num_examples: 380 |
| download_size: 309433429 |
| dataset_size: 309506565 |
| --- |
| |
|
|
| # Dataset Card for IndoorCrowd |
|
|
| ## Dataset Description |
|
|
| - **Paper:** IndoorCrowd: A Multi-Scene Dataset for Human Detection, Segmentation, and Tracking with an Automated Annotation Pipeline (CVPR 2026 Submission) |
|
|
| ### Dataset Summary |
|
|
| IndoorCrowd is a multi-scene dataset designed for indoor human detection, instance segmentation, and multi-object tracking. It captures diverse challenges such as viewpoint variation, partial occlusion, and varying crowd density across four distinct campus locations (ACS-EC, ACS-EG, IE-Central, R-Central). Faces are explicitly blurred to preserve privacy, making it suitable for safe research into intelligent crowd management and behaviour tracking. |
|
|
| The dataset consists of 31 videos sampled at 5 FPS, totalling 9,913 frames. |
|
|
| ### Subsets |
|
|
| 1. **Object Detection and Segmentation:** 9,913 frames featuring bounding boxes and instance segmentation masks. Includes a rigorously annotated 620-frame pure-human control subset for foundation-model benchmarking. |
| 2. **Multi-Object Tracking (MOT):** A 2,552-frame tracking subset providing continuous identity tracks following the MOTChallenge format. |
|
|
| ### Supported Tasks |
|
|
| - `object-detection`: Detecting human bounding boxes (Baselines benchmarked: YOLOv8n, YOLOv26n, RT-DETR-L). |
| - `image-segmentation`: Generating instance-level masks for people in crowded indoor geometries. |
| - `video-object-tracking`: Maintaining human identity across consecutive frames via tracking algorithms (Baselines benchmarked: ByteTrack, BoT-SORT, OC-SORT). |
|
|
| ## Dataset Creation |
|
|
| ### Curation Rationale |
|
|
| Outdoor datasets currently dominate development. Indoor environments introduce a new set of challenges like camera view obstructions (pillars, furniture), structural occlusions, near-to-distal scale variance, and abrupt density fluctuations. |
|
|
| ### Annotations |
|
|
| Annotations were produced using a semi-automated pipeline: |
| 1. **Auto-labelling:** Uses foundation models such as SAM3, GroundingSAM, and EfficientGroundingSAM to generate initial candidate masks and tracklets. |
| 2. **Human Correction:** Expert human reviewers used SAM 2.1 to manually delete false positives, append missing masks, correct identity switches, and linearly interpolate gaps, ensuring high-fidelity ground truth. |
|
|
| ### Data Splits |
|
|
| The dataset provides varied crowd density regimes: |
| - **ACS-EC:** A dense multi-level atrium setting with small instance scales and high occlusion ($79.3\%$ dense frames). |
| - **ACS-EG:** A narrow ground-level corridor with substantial person scale variations lengthways. |
| - **IE-Central:** An intermediate seating/entrance hall environment. |
| - **R-Central:** An overhead-view atrium with prominent structural columns causing regular occlusions. |
|
|
| ### Personal and Sensitive Information |
|
|
| All human faces in the raw footage have been strictly blurred by an automated de-identification pipeline prior to release. No audio, demographic attributes, or personal identifiers are collected. |
|
|
| ## Additional Information |
|
|
| ### Licensing Information |
|
|
| The dataset is released under a license restricting its use strictly to non-commercial computer vision research. It prohibits surveillance and any re-identification of individuals. |
|
|
