Object Detection and Tracking
This work addresses the need for quick and precise object detection in computer vision applications, but it appears incremental as it builds on existing deep learning methods without specifying novel breakthroughs.
The research tackled the problem of inefficient object detection by integrating a modern deep learning technique to achieve high accuracy with real-time performance, using a challenging public dataset for training.
Efficient and accurate object detection is an important topic in the development of computer vision systems. With the advent of deep learning techniques, the accuracy of object detection has increased significantly. The project aims to integrate a modern technique for object detection with the aim of achieving high accuracy with real-time performance. The reliance on other computer vision algorithms in many object identification systems, which results in poor and ineffective performance, is a significant obstacle. In this research, we solve the end-to-end object detection problem entirely using deep learning techniques. The network is trained using the most difficult publicly available dataset, which is used for an annual item detection challenge. Applications that need object detection can benefit the system's quick and precise finding.