README.md

metadata

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.

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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

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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.

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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)

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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.

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Direct download

• https://huggingface.co/datasets/Mihai20/kth-hog-npz/resolve/main/hog_aug_4framegap.npz

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Example usage (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.