Project that combines optical flow and object detection using the Kirsch operator.

Introduction

Welcome to my academic project report on moving object detection based on Kirsch operator combined with optical flow. This project focuses on the implementation of a system that can accurately detect moving objects in a video stream. The combination of Kirsch operator and optical flow techniques allows for improved accuracy and efficiency in detecting moving objects in real-time.

Problem Statement

The existing methods for moving object detection often suffer from issues such as low accuracy, high computational complexity, and sensitivity to environmental conditions. These limitations make it challenging to deploy such systems in real-world applications where accurate and efficient moving object detection is crucial.

Existing System

The existing systems for moving object detection typically rely on background subtraction or frame differencing techniques. While these methods can work well in certain situations, they often struggle to accurately detect moving objects in complex backgrounds or under varying lighting conditions.

Disadvantages

Some of the disadvantages of the existing systems include:
– Low accuracy in detecting moving objects
– High computational complexity
– Sensitivity to environmental conditions
– Inability to handle complex backgrounds or varying lighting conditions

Proposed System

Our proposed system combines the Kirsch operator and optical flow techniques to improve the accuracy and efficiency of moving object detection. The Kirsch operator is used to enhance the edges of moving objects in the video stream, while optical flow is used to track the motion of these objects over time.

Advantages

Some of the advantages of our proposed system include:
– Higher accuracy in detecting moving objects
– Lower computational complexity
– Improved performance under varying environmental conditions
– Ability to handle complex backgrounds and varying lighting conditions

Features

Some of the key features of our proposed system include:
– Real-time moving object detection
– Robust performance in complex environments
– Accurate tracking of moving objects
– Low computational overhead
– Easy integration with existing surveillance systems

Conclusion

In conclusion, our project on moving object detection based on Kirsch operator combined with optical flow offers a promising solution to the limitations of existing systems. By leveraging the strengths of both techniques, we have developed a system that can accurately detect moving objects in real-time, even in complex environments. We believe that this project has the potential to significantly impact the field of computer vision and surveillance systems, and we look forward to further refining and deploying our system in practical applications.