Object Detection and Tracking

TL;DR

This paper presents a deep learning-based object detection system designed for real-time, high-accuracy performance, addressing limitations of traditional methods by using end-to-end training on challenging datasets.

Contribution

It introduces a fully deep learning approach for object detection that achieves high accuracy and real-time performance, improving over traditional multi-algorithm systems.

Findings

Achieved high detection accuracy on challenging datasets

Enabled real-time object detection performance

Improved efficiency over traditional methods

Abstract

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.