Upload 2 files

README.md

@@ -0,0 +1,93 @@
+---
+task_categories:
+- object-detection
+- text-classification
+- feature-extraction
+language:
+- ko
+tags:
+- homecam
+- video
+- audio
+- npy
+size_categories:
+- 100B<n<1T
+---
+
+## Dataset Overview
+
+- The dataset is designed to support the development of machine learning models for detecting daily activities, violence, and fall down scenarios from combined audio and video sources.
+- The preprocessing pipeline leverages audio feature extraction, human keypoint detection, and relative positional encoding to generate a unified representation for training and inference.
+	- Classes:
+	    - 0: Daily - Normal indoor activities
+	    - 1: Violence - Aggressive behaviors
+	    - 2: Fall Down - Sudden falls or collapses
+	- Data Format:
+	    - Stored as `.npy` files for efficient loading and processing.
+	    - Each `.npy` file is a tensor containing concatenated audio and video feature representations for a fixed sequence of frames.
+
+## Dataset Preprocessing Pipeline
+![Data Preprocessing](./pics/data-preprocessing.png)
+- The dataset preprocessing consists of a multi-step pipeline to extract and align audio features and video keypoints. Below is a detailed explanation of each step:
+
+### Step 1: Audio Processing
+1. WAV File Extraction:
+	- Audio is extracted from the original video files in WAV format.
+2. Frame Splitting:
+	- The audio signal is divided into 1/30-second segments to synchronize with video frames.
+3.	MFCC Feature Extraction:
+	- Mel-Frequency Cepstral Coefficients (MFCC) are computed for each audio segment.
+	- Each MFCC output has a shape of 13 x m, where m represents the number of frames in the audio segment.
+
+### Step 2: Video Processing
+1.	YOLO Object Detection:
+	- Detects up to 3 individuals in each video frame using the YOLO model.
+	- Outputs bounding boxes for detected individuals.
+2.	MediaPipe Keypoint Extraction:
+	- For each detected individual, MediaPipe extracts 33 keypoints, each represented as (x, y, z, visibility), where:
+	    - x, y, z : Spatial coordinates.
+	    - visibility : Confidence score for the detected keypoint.
+3.	Keypoint Filtering:
+	- Keypoints 1, 2, and 3 (eyebrow-specific) are excluded.
+	- Keypoints are further filtered by visibility threshold(0.5) to ensure reliable data.
+    - Visibility property is excluded in further calculations.
+4.	Relative Positional Encoding:
+	- For the remaining 30 keypoints, relative positions of the 10 most important keypoints are computed.
+	- These relative positions are added as additional features to improve context-aware modeling.
+5. Feature Dimensionality Adjustment:
+	- The output is reshaped to (n, 30*3 + 30, 3), where n is the number of frames.
+
+### Step 3: Audio-Video Feature Concatenation
+1. Expansion:
+	- Video keypoints are expanded to match the audio feature dimensions, resulting in a tensor of shape (1, 1, 4).
+2.	Concatenation:
+	- Audio (13) and video (12) features are concatenated along the feature axis.
+	- The final representation has a shape of (n, 120, 13+12), where n is the number of frames.
+
+### Data Storage
+- The final processed data is saved as `.npy` files, organized into three folders:
+	- `0_daily/`: Contains data representing normal daily activities.
+	- `1_violence/`: Contains data representing violent scenarios.
+	- `2_fall_down/`: Contains data representing falling events.
+
+## Dataset Description
+
+- This dataset provides a comprehensive representation of synchronized audio and video features for real-time activity recognition tasks. 
+- The combination of MFCC audio features and MediaPipe keypoints ensures the model can accurately detect and differentiate between the defined activity classes.
+
+- Key Features:
+	1.	Multimodal Representation:
+	    - Audio and video modalities are fused into a single representation to capture both sound and motion dynamics.
+	2.	Efficient Format:
+	    - The `.npy` format ensures fast loading and processing, suitable for large-scale training.
+	3.	Real-World Applications:
+	    - Designed for safety systems, healthcare monitoring, and smart home applications.
+        - Adaptation in `SilverAssistant` project: [HuggingFace SilverAssistant Model](https://huggingface.co/SilverAvocado/SilverAssistant)
+
+- This dataset enables the development of robust multimodal models for detecting critical situations with high accuracy and efficiency.
+
+## Data Sources
+- Source 1: [시니어 이상행동 영상 AI Hub](https://www.aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&aihubDataSe=data&dataSetSn=167)
+- Source 2: [이상행동 cctv 영상 AI Hub](https://www.aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&aihubDataSe=data&dataSetSn=171)
+- Source 3: [멀티모달 영상](https://www.aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&aihubDataSe=realm&dataSetSn=58)
+