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Drone Video Horse Recognition

This project uses deep learning to analyze drone video footage and recognize individual horses based on their visual features. The program processes two datasets:

  • Training Dataset: Contains X_1 to X_i folders, each representing a unique horse.
  • Testing Dataset: Contains Y_1 to Y_i folders, each also representing a horse, some of which overlap with the training dataset.

The system computes feature vectors for each horse, compares them across datasets on a per-second basis, and generates similarity metrics for ranking and evaluation.

Features

  • Feature Extraction: Uses a pre-trained Vision Transformer (DINOv2) to extract high-dimensional feature vectors from video frames.
  • Per-Second Analysis: Aggregates video frames into per-second averages for more robust feature comparison.
  • Cosine Similarity: Measures the similarity between horses from the training and testing datasets.
  • Visualization: Generates a histogram and confusion matrix to represent horse similarity.

Input Data

Training Dataset

  • Folders named X_1, X_2, ..., X_i represent individual horses.
  • Each folder contains video frames (images) of the corresponding horse.

Testing Dataset

  • Folders named Y_1, Y_2, ..., Y_i represent individual horses.
  • These folders also contain video frames for each horse, with some horses overlapping with the training dataset.

Per-Second Cosine Similarity

For each second of video, the average feature vector of the frames is calculated

Overall Similarity

The overall similarity between X_k and Y_j is the average cosine similarity across all seconds:

overall similarity

where T is the total number of seconds compared.

Output

Histogram

A bar chart showing the similarity scores between each horse in the training dataset and the horses in the testing dataset.

  • X-axis: Horses from the testing dataset.
  • Y-axis: Similarity scores.
  • Highlighted Bar: The best-matching horse.

Confusion Matrix

A heatmap illustrating the similarity between horses in the training and testing datasets.

  • Rows: True labels (training dataset horses).
  • Columns: Predicted labels (testing dataset horses).
  • Values: Average similarity scores.

Installation and Usage

Prerequisites

  • Python 3.8+
  • Required libraries:
    • torch
    • transformers
    • Pillow
    • numpy
    • matplotlib
    • seaborn
    • tqdm

Steps

  1. Clone this repository:

    git clone https://github.com/liu9756/Drone-Video-Horse-recognization.git
cd Drone-Video-Horse-recognization

  2. Install dependencies:

    pip install -r requirements.txt

  3. Prepare your datasets:

    • Place training folders (X_1, X_2, ...) in a directory.
    • Place testing folders (Y_1, Y_2, ...) in a separate directory.

  4. Run the script:

    python Similarity_compare.py

  5. View the generated images:

    • similarity_histogram.png: Histogram showing similarity scores.
    • confusion_matrix.png: Confusion matrix visualizing horse recognition results.

Example Outputs

Similarity Histogram

Similarity Histogram

This histogram highlights the best-matching horse in red, making it easy to identify which horse in the testing dataset most closely resembles a given horse from the training dataset.

Confusion Matrix

Confusion Matrix

The confusion matrix provides an overview of similarity scores between horses in the training and testing datasets, helping to evaluate the recognition performance.