Detection, Recognition, and Tracking: A Survey

TL;DR

This survey reviews recent techniques in computer vision for object detection, recognition, and tracking, highlighting advances and challenges in developing algorithms that mimic human perception in analyzing images and videos.

Contribution

It provides a comprehensive overview of novel methods in object detection, recognition, and tracking, emphasizing their applications and recent developments.

Findings

Recent algorithms improve detection accuracy

Tracking methods enhance object movement analysis

Challenges remain in real-time processing

Abstract

For humans, object detection, recognition, and tracking are innate. These provide the ability for human to perceive their environment and objects within their environment. This ability however doesn't translate well in computers. In Computer Vision and Multimedia, it is becoming increasingly more important to detect, recognize and track objects in images and/or videos. Many of these applications, such as facial recognition, surveillance, animation, are used for tracking features and/or people. However, these tasks prove challenging for computers to do effectively, as there is a significant amount of data to parse through. Therefore, many techniques and algorithms are needed and therefore researched to try to achieve human like perception. In this literature review, we focus on some novel techniques on object detection and recognition, and how to apply tracking algorithms to the detected features to track the objects' movements.