Detection, Recognition, and Tracking: A Survey
It provides an overview for researchers and practitioners in computer vision, but is incremental as it synthesizes existing methods without introducing new results.
This survey reviews techniques for object detection, recognition, and tracking in computer vision, addressing the challenge of enabling computers to achieve human-like perception in tasks such as facial recognition and surveillance.
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.