| --- |
| language: |
| - en |
| tags: |
| - human-action-recognition |
| - har |
| - hog |
| - cv |
| - kth |
| license: mit |
| --- |
| |
| # KTH HOG Features (NPZ) |
|
|
| This repository hosts a precomputed HOG feature dataset derived from the KTH Human Action Recognition (KTH-HAR) videos. It is used by the CNN-for-HAR Streamlit demo and allows fast inference/training without decoding raw videos at runtime. |
|
|
| ## What this file contains |
|
|
| `hog_aug_4framegap.npz` (~3GB) includes: |
|
|
| - `features`: shape `(N, T*3780)` float32 |
| - `bboxes`: shape `(N, T, 4)` float32, normalized `(cx, cy, w, h)` per frame |
| - `labels`: shape `(N,)` int64 |
| - `metadata`: list of dicts with `video_key`, `subject`, `action`, `split`, `group_idx`, `aug_idx`, `aug_name`, `frame_indices` |
| - `config`: dict with generation settings (see below) |
|
|
| Each sample is a clip of **T frames**, stored as a flattened HOG vector. HOG is computed on 64×128 crops (OpenCV HOG defaults), which yields **3780 features per frame**. |
|
|
| --- |
|
|
| ## How the file is generated (pipeline) |
|
|
| This `.npz` is produced by `extract_hog_augmented.py` in the main repo: |
|
|
| 1. Load **bbox metadata JSON** (groups of frame indices per clip). |
| 2. For each video: |
| - Read only the needed frames. |
| 3. For each clip: |
| - **Always include the original version** (no augmentation). |
| - For **train clips only**, generate additional augmented versions (`num_aug - 1`). |
| 4. For every variant: |
| - Crop person bbox → resize to 64×128 |
| - Optional flip + photometric changes |
| - Compute HOG |
| - Save HOG + normalized bbox info |
|
|
| --- |
|
|
| ## What `num_aug=4` means |
| |
| When `num_aug=4`, each **train clip** becomes **4 variants**: |
|
|
| 1. `orig` — no augmentation |
| 2. `flip` — horizontal flip only |
| 3. `jit0` — random jitter + photometric + possible blur/noise |
| 4. `jit1` — another random jitter + photometric + possible blur/noise |
|
|
| Test clips always have **only the original version**. |
|
|
| --- |
|
|
| ## Augmentations used in `jit*` |
| |
| The `jit*` variants apply a random combination of: |
|
|
| - **BBox jitter**: translation `(dx, dy)` + scale |
| - **Random flip** (50%) |
| - **Photometric changes**: contrast (`alpha`), brightness (`beta`) |
| - **Gamma** |
| - **Noise** (optional) |
| - **Blur** (optional) |
|
|
| ### `mild` profile (default) |
| - scale: 0.92–1.08 |
| - dx: ±5, dy: ±3 |
| - alpha: 0.85–1.15 |
| - beta: -15…+15 |
| - gamma: 0.95–1.05 |
| - noise: std 2.0 with p=0.5 |
| - blur: p=0.15 (kernel 3) |
|
|
| ### `strong` profile |
| - scale: 0.88–1.15 |
| - dx: ±8, dy: ±6 |
| - alpha: 0.75–1.25 |
| - beta: -25…+25 |
| - gamma: 0.85–1.15 |
| - noise: std 4.0 with p=0.7 |
| - blur: p=0.25 (kernel 3) |
|
|
| --- |
|
|
| ## What `frame_gap=4` means |
| |
| The **frame gap** is defined in the bbox JSON (not in the extractor). |
| Each clip groups frames spaced by a fixed gap (e.g., every 4th frame). |
| That’s why this file is named `hog_aug_4framegap.npz`. |
| |
| --- |
| |
| ## Direct download |
| - https://huggingface.co/datasets/Mihai20/kth-hog-npz/resolve/main/hog_aug_4framegap.npz |
| |
| --- |
| |
| ## Example usage (Python) |
| |
| ```python |
| import numpy as np |
| |
| data = np.load("hog_aug_4framegap.npz", allow_pickle=True) |
| features = data["features"] |
| bboxes = data["bboxes"] |
| labels = data["labels"] |
| metadata = data["metadata"].tolist() |
| config = data["config"].item() # contains num_aug, profile, etc. |
| ``` |
| |
| ## Notes |
| - Intended for inference + demo usage without raw video decoding. |
| - The original videos are not required to use this .npz. |
| - Augmentation settings are saved in config inside the file. |
