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.gitignore

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+convert_oops_via_to_csv.py

README.md

@@ -270,7 +270,7 @@ configs:
 
 # OmniFall: A Unified Benchmark for Staged-to-Wild Fall Detection
 
-This repository contains the annotation and split definitions for OmniFall, a comprehensive benchmark that unifies eight public indoor fall datasets under a consistent ten-class annotation scheme, complemented by the OOPS-Fall benchmark of genuine accidents captured in the wild.
+This repository contains the annotation and split definitions for OmniFall, a comprehensive benchmark that unifies eight public indoor fall datasets and the OOPS-Fall benchmark of genuine accidents captured in the wild under a sixteen-class annotation scheme. The staged datasets use a core subset of ten classes, while OOPS-Fall utilizes the full sixteen-class taxonomy.
 
 [OmniFall: A Unified Staged-to-Wild Benchmark for Human Fall Detection](https://arxiv.org/abs/2505.19889)
 
@@ -284,7 +284,7 @@ Falls are the leading cause of fatal injuries among older adults worldwide. Whil
 
 OmniFall addresses three critical limitations in current fall detection research:
 
-1. **Unified Taxonomy:** Rather than binary fall/no-fall classification, we provide a consistent ten-class scheme across datasets that distinguishes transient actions (fall, sit down, lie down, stand up) from their static outcomes (fallen, sitting, lying, standing).
+1. **Unified Taxonomy:** Rather than binary fall/no-fall classification, we provide a sixteen-class scheme that distinguishes transient actions (fall, sit down, lie down, stand up) from their static outcomes (fallen, sitting, lying, standing). Staged datasets use the core ten classes (0-9), while OOPS-Fall utilizes the full sixteen classes (0-15) to capture additional activities observed in wild scenarios (kneel_down, kneeling, squat_down, squatting, crawl, jump). The extended classes are infrequent and can be treated as "other" (class 9) for compatibility.
 
 2. **Combined Benchmark:** We unify eight public datasets (14+ hours of video, 112 subjects, 31 camera views) into a single benchmark with standardized train/val/test splits.
 
@@ -324,7 +324,7 @@ path/to/clip,class_id,start_time,end_time,subject_id,camera_id,dataset_name
 
 Where:
 - `path`: Relative path to the video, given the respective dataset root.
-- `label`: Class ID (0-9) corresponding to one of the ten activity classes:
+- `label`: Class ID (0-15) corresponding to one of sixteen activity classes:
   - 0: walk
   - 1: fall
   - 2: fallen
@@ -335,14 +335,21 @@ Where:
   - 7: stand_up
   - 8: standing
   - 9: other
+  - 10: kneel_down
+  - 11: kneeling
+  - 12: squat_down
+  - 13: squatting
+  - 14: crawl
+  - 15: jump
 - `start`: Start time of the segment (in seconds)
 - `end`: End time of the segment (in seconds)
 - `subject`: Subject ID
 - `cam`: Camera view ID
 - `dataset`: Name of the dataset
 
-For OOPS-Fall, only fall segments and non-fall segments are labeled; non-falls are labels as "other", independent of the underlying content, as long as it is not a fall. 
-Cam and subject ids in OOPS-Fall are -1.
+**Note on class usage:** Staged datasets (caucafall, cmdfall, edf, gmdcsa24, le2i, mcfd, occu, up_fall) use only the core ten classes (0-9), as these datasets almost never contain situations where the extended classes (10-15) apply. OOPS-Fall uses the full sixteen-class taxonomy to capture the diverse activities in genuine accident videos. The extended classes are infrequent even in OOPS-Fall and can be treated as "other" (class 9) when working with models trained on staged datasets only.
+
+Cam and subject ids in OOPS-Fall are -1, since every video differs from the others and there are no consistent camera views or subjects.
 
 ### Split Format
 
@@ -434,7 +441,9 @@ for split_name, split_data in [("CV", cv_split), ("CS", cs_split)]:
 
 In this section we provide additional information about the labelling process to provide as much transparency as possible.
 
-There are 10 labels:
+The benchmark uses a sixteen-class taxonomy. Staged datasets use classes 0-9, while OOPS-Fall uses the full range 0-15.
+
+### Core Classes (0-9, all datasets)
 
 - **`0|walk`** - Move around, including jogging and running and "drunk walking", but only if it is not part of some special exercise like pulling your knees up. Not when pushing a large object like a chair, but included carrying something small like an apple.
 - **`1|fall`** - The act of falling (from any previous state). Includes falling on a bed, if the process is not a controlled lying down with arms as support.
@@ -447,6 +456,17 @@ There are 10 labels:
 - **`8|standing`** - Standing around without walking.
 - **`9|other`** - Any other activity, including e.g. walking while pushing an object like a chair.
 
+### Extended Classes (10-15, OOPS-Fall only)
+
+These classes capture additional activities observed in genuine accident videos. They are infrequent even in OOPS-Fall and do not occur in staged datasets.
+
+- **`10|kneel_down`** - Transitioning from standing or another posture to a kneeling position.
+- **`11|kneeling`** - Being in a kneeling position (static posture).
+- **`12|squat_down`** - Transitioning to a squatting position.
+- **`13|squatting`** - Being in a squatting position (static posture).
+- **`14|crawl`** - Crawling on hands and knees or similar locomotion on the ground.
+- **`15|jump`** - Jumping action, including vertical jumps and jumps from elevated positions.
+
 ## Motion Types
 
 There are two types of motions, **dynamic** ones like `walk` or `stand_up` and **static** ones like `fallen`, `sitting`, `lying`.