Object Detection And Tracking

This project implements a real-time object detection and tracking system leveraging OpenCV and TensorFlow. By combining advanced neural network models with efficient image processing techniques, the system ensures reliable performance without compromising on affordability or ease of use.

Features

• Real-Time Detection and Tracking: Identifies and monitors objects in motion with minimal latency.
• Multi-Object Tracking: Capable of segmenting and tracking multiple objects simultaneously.
• Cost-Effective Design: Utilizes affordable hardware and software to ensure accessibility.
• Versatile Applications:
  • Security surveillance
  • Traffic flow analysis and accident detection
  • Retail customer behavior analysis
  • Video abstraction and editing
  • Military target tracking

Tools and Techniques

• OpenCV: Utilized for image processing and computer vision tasks, enabling efficient handling of video streams and real-time processing.
• TensorFlow: Implements neural network models for robust object detection capabilities.
• Python: The primary programming language used for integrating OpenCV and TensorFlow functionalities.
• Pre-trained Models: Leveraging models like Faster R-CNN and MobileNet SSD for object detection.
• Object Detection:
  • Faster R-CNN: Achieves high precision in object detection but with slower processing speeds.
  • MobileNet SSD: Offers faster detection speeds with slightly lower precision.
• Image Segmentation: Isolates objects of interest from video scenes to facilitate accurate tracking.
• Real-Time Tracking: Utilizes motion estimation techniques to maintain continuous tracking of objects, handling challenges like occlusion and orientation changes.

Results

• Comparative Analysis:
  • Faster R-CNN:
    • Precision: Achieved 93% accuracy in object detection.
    • Performance: Slower processing speed due to the complexity of the model.
  • MobileNet SSD:
    • Precision: Reached 84% accuracy.
    • Performance: Faster detection speeds, making it suitable for real-time applications.
• System Performance:
  • Successfully implemented a real-time object detection and tracking system.
  • Demonstrated the ability to track multiple objects with high accuracy on continuous video streams.
  • Balanced performance and cost-effectiveness, ensuring suitability for a wide range of practical applications.

Installation

1. Clone the Repository:

   git clone https://github.com/adityarohatgi11/Object_Detection_ML.git
   cd Object_Detection_ML

2. Install Dependencies:

   pip install -r requirements.txt

Future Work

• Enhance Tracking Accuracy: Improve performance in scenarios with occlusions and rapid object movements.
• Hardware Acceleration: Integrate support for GPUs (e.g., NVIDIA GPUs) and other accelerators (e.g., Coral TPUs) to boost processing speeds.
• Model Customization: Develop and incorporate customizable models tailored for specific domains and applications.
• Scalability: Expand the system to handle larger-scale deployments with increased object counts and higher resolution video streams.

License

This project is licensed under the MIT License. You are free to use, modify, and distribute this software under the terms of the license.

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