People Detection Using Artificial Neural Networks

Project Overview

This project demonstrates the implementation of an Artificial Neural Network (ANN) to detect people in urban environments using custom-extracted image features. The ANN is trained on a dataset consisting of images categorized into scenes with people and without people but with animals.

Dataset

The dataset comprises 200 images split into two main categories:

V1: Images with people and animals (100 images)
V2: Images with animals but without people (100 images)

Each image is processed through a custom tool that divides the image into 128x128 grids and labels each grid with one of the following classifications: Absent (A), Animal (N), Noise, and People (P). The grids are stored in respective folders according to their labels.

Folder Structure

dataset/
- V1/ (with people and animals)
  - output/
    - A/ (Absent)
    - N/ (Animal)
    - Noise/
    - P/ (People)
- V2/ (without people but with animals)
  - output/
    - A/ (Absent)
    - N/ (Animal)
    - Noise/
    - P/ (People)

Image Features

The following features are extracted from each grid for training the neural network:

Color Channels (R, G, B): Basic color information which is crucial for identifying elements like clothing or skin tones.
RGB Mean: Gives a general idea of the predominant color tone, useful for differentiating background.
RGB Mode: Helps identify the most common colors, useful for recognizing typical clothing colors.
RGB Variance & Standard Deviation: Measures color variability which can highlight significant details within the grid.
Color Histogram: Analyzes the distribution of color intensities, essential for segmentation based on color.
Gray Level Co-occurrence Matrix Properties: Provides texture information from the image which is critical for pattern recognition.
Local Binary Patterns: Useful for texture classification and differentiating between objects and their surroundings.
Histogram of Oriented Gradients (HOG): Effective for detecting human forms in various poses.
Peak Local Max: Detects key points in the image, aiding in feature matching and scene understanding.

Labeling Images

Data Splitting and Cross-Validation

We employ a standard 80-20 split for training and testing our model, ensuring the dataset is diverse and representative of real-world scenarios. Additionally, we use 5-fold cross-validation to verify the robustness of our ANN model.

Finding the Best Hyperparameters

We explore various combinations of hyperparameters to optimize our ANN's performance. This includes testing different numbers of layers, neurons, learning rates, and more.

Training Methodology

Our training approach focuses on minimizing overfitting while maximizing the predictive accuracy of our neural network by adjusting epochs, batch sizes, and regularization methods.