Abstract
The problem of reidentification of a person in multiple cameras is a hot topic in computer vision research. The issue is with the consistent identification of a person in multiple cameras from different viewpoints and environmental conditions. Many computer vision researchers have been looking into methods that can improve the reidentification of people for many real-world purposes. There are new methods each year that expand and explore new concepts and improve the accuracy of reidentification. This paper will look at current developments and the past tends to find what has been done and what is being done to solve this problem. This paper will start off by introducing the topic as well as covering the basic concepts of the reidentification problem. Next, it will cover common datasets that are used in today's research. Then it will look at evaluation techniques. Then this paper will start to describe simple techniques that are used followed by the current deep learning techniques. This paper will cover how these techniques are used, what are some of their weaknesses and their strengths. It will conclude with an overview of some of the best models and show which models have the most promise and which models should be avoided.
Highlights
The problem of accurately locating a target in either multiple photographs, a video stream, or from a collection of cameras at different angles has been a hot topic in computer vision research
The most common type of comparison found in the literature between each model is mean average precision, Rank 1, Rank 5, and Rank 10
There have been many techniques used to perform ReID in images. These techniques can be simple as color feature extraction, shapes, gait analysis, or complex as multi-layer deep learning models that use local and global features for the analysis
Summary
The problem of accurately locating a target in either multiple photographs, a video stream, or from a collection of cameras at different angles has been a hot topic in computer vision research. Re-identification (ReID) has many useful realworld applications. These applications can range from security systems and surveillance to automatically tagging people in photographs on social media. ReID can monitor a location and track a suspect as they traverse different areas. ReID can significantly reduce the time and cost in apprehending and finding a potential target. ReID can be used to monitor customer’s activity in a store to aid in streamlining the geospatial layout of a store’s products. ReID can be a potent tool for decision-makers and planners
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