Object Detection and Tracking with Faster R-CNN

Project Overview

This project focuses on real-time object detection and tracking using the Faster R-CNN model, prioritizing accuracy and precise object identification over speed. By leveraging the COCO 2017 dataset, the project aims to perform high-quality object detection and tracking, with particular attention to the accurate classification and localization of objects across frames.

Model Selection

For this project, Faster R-CNN is chosen as the pre-trained model for object detection due to its optimal balance between accuracy and speed. Unlike models such as YOLO (You Only Look Once) and SSD (Single Shot MultiBox Detector), Faster R-CNN excels at precision, making it ideal for tasks where the identification of smaller or more complex objects is critical.

Faster R-CNN operates in two stages:

1. Region Proposal Network (RPN): Generates potential bounding boxes for objects in an image.
2. Object Classification and Refinement: Refines these boxes and classifies the objects within them, ensuring high accuracy and low false positive rates.

The integration with deep feature extractors such as ResNet further enhances its ability to capture intricate details, making it well-suited for complex object detection tasks.

Dataset

The COCO 2017 dataset is used for training and testing the model. The COCO dataset is well-known in the machine learning community for its diverse collection of images and annotations. It includes various categories of objects in their natural contexts, making it suitable for real-time object detection and tracking.

Dataset Challenges

• The COCO dataset, while comprehensive, did not fully capture all edge cases required for more advanced tracking scenarios.
• Memory limitations required using FiftyOne to efficiently load and work with the dataset, rather than directly downloading the entire dataset from the official COCO website.

Object Detection Process

1. FiftyOne Integration: The project uses FiftyOne, a tool for visualizing and working with machine learning datasets, to load the COCO dataset and make predictions.
2. Model Inference: A pre-trained Faster R-CNN model is loaded using PyTorch, and inference is performed on a random subset of the training dataset.
3. Prediction and Visualization: Predictions, including labels, bounding boxes, and confidence scores, are added to the images and visualized in FiftyOne. The results are compared to ground truth annotations, showing the model's performance.

Object Tracking

• CentroidTracker: A custom class is implemented for tracking detected objects across frames. This tracker maintains the identity of objects over time, crucial for applications involving object tracking.
• Tracking Implementation: The tracker is updated with each detection frame, and its performance is visualized alongside detection predictions in FiftyOne.

Example Output

The visual output displays:

• Ground Truth: The manually annotated objects in the dataset.
• Predictions: The objects predicted by the Faster R-CNN model.
• Tracking: The tracking information showing how objects are identified across frames.

Challenges Faced

1. Model Complexity and Speed: Faster R-CNN, while accurate, is computationally intensive. It struggles to meet real-time requirements due to its relatively slower inference times compared to models like YOLO or SSD.
2. Dataset Limitations: The COCO dataset did not cover all edge cases or specific scenarios required for tracking across diverse environments.
3. Memory Limitations: Handling the large COCO dataset directly required significant memory resources, leading to the decision to use FiftyOne for dataset management.
4. Generalization: The model performed well on the COCO dataset but showed limited generalization to new, unseen datasets.

Future Directions

• Optimization for Real-time Applications: Explore model compression techniques or investigate lighter models like YOLO or SSD for faster inference while maintaining acceptable accuracy.
• Domain Adaptation: Train the model on additional datasets or apply domain adaptation techniques to improve generalization to new environments.
• Edge Case Handling: Incorporate more edge cases into the training process to handle more complex and diverse scenarios for object detection and tracking.

Installation

To set up the project environment, use the following steps:

1. Clone the Repository:

   git clone https://github.com/yourusername/object-detection-tracking.git
   cd object-detection-tracking

2. Install Dependencies:

   pip install -r requirements.txt

3. Download the COCO 2017 Dataset: Use FiftyOne to load the dataset efficiently:

   import fiftyone as fo
   # Load the COCO dataset using FiftyOne

4. Run the Project: Execute the following command to start object detection and tracking:

   python run_detection_tracking.py

Contributing

Feel free to fork the repository and submit pull requests. Contributions to improving the model, dataset, or tracking algorithms are welcome!

References

• COCO Dataset: https://cocodataset.org/
• FiftyOne: https://docs.voxel51.com/
• Faster R-CNN: https://arxiv.org/abs/1506.01497