Algorithm for tracking klt features implemented on embedded hardware.
Introduction
The KLT (Kanade-Lucas-Tomasi) feature tracking algorithm is a popular method used in computer vision for tracking features in sequential image frames. This algorithm is known for its robustness and accuracy in tracking various types of features. In this project work, we will focus on implementing the KLT feature tracking algorithm in embedded hardware, specifically in the context of real-time video processing applications.
Problem Statement
The traditional implementation of the KLT feature tracking algorithm requires significant computational resources, making it challenging to implement in embedded systems with limited processing capabilities. This limitation hinders the use of the algorithm in applications where real-time processing is essential, such as surveillance systems, autonomous vehicles, and robotics.
Existing System
In the existing system, the KLT feature tracking algorithm is implemented on general-purpose computers or GPUs, which offer high computational power and memory resources. However, these systems are not suitable for embedded applications due to their size, power consumption, and cost constraints. As a result, there is a need for a more efficient implementation of the KLT algorithm in embedded hardware to enable real-time feature tracking in resource-constrained environments.
Disadvantages
The main disadvantages of the existing system are:
– High computational complexity: The traditional KLT algorithm requires intensive calculations for feature tracking, making it unsuitable for embedded hardware.
– High memory requirements: Storing the pixel values for feature tracking in each frame consumes a significant amount of memory, which is limited in embedded systems.
– Limited processing capabilities: Embedded systems have constraints on processing power, which makes it challenging to run complex algorithms like the KLT feature tracking algorithm.
Proposed System
In this project work, we propose to implement the KLT feature tracking algorithm in embedded hardware using optimization techniques to reduce computational complexity and memory requirements. By leveraging the parallel processing capabilities of modern embedded processors, we aim to achieve real-time feature tracking performance while maintaining low power consumption and cost-effectiveness.
Advantages
The proposed system offers the following advantages:
– Real-time performance: By optimizing the algorithm for embedded hardware, we can achieve real-time feature tracking in applications requiring fast processing.
– Low power consumption: Embedded systems are designed for low power operation, making them ideal for battery-powered devices or energy-efficient applications.
– Cost-effective: Embedded hardware is typically cheaper than general-purpose computers or GPUs, making it a cost-effective solution for implementing the KLT algorithm in resource-constrained environments.
Features
The key features of the proposed system include:
– Optimization techniques: We will explore various optimization techniques, such as parallelization, loop unrolling, and memory management, to reduce the computational complexity of the KLT algorithm.
– Hardware acceleration: Leveraging the hardware acceleration capabilities of embedded processors, such as GPUs, FPGAs, or dedicated coprocessors, to offload computation-intensive tasks and improve performance.
– Real-time processing: Implementing the KLT algorithm in embedded hardware to achieve real-time feature tracking for applications requiring fast response times.
Conclusion
In conclusion, the proposed implementation of the KLT feature tracking algorithm in embedded hardware holds promise for enabling real-time feature tracking in resource-constrained environments. By optimizing the algorithm for embedded processors and leveraging hardware acceleration capabilities, we can overcome the limitations of the existing system and provide a cost-effective solution for applications requiring efficient feature tracking. The success of this project work will contribute to the advancement of computer vision technology in embedded systems and open up new possibilities for real-time image processing applications.