Image Recreating in improving the Performance of Architectures for Person Re-identification

kground and Objectives: Re-identifying individuals due to its capability to match a person across non-overlapping cameras is a significant application in computer vision. However, it presents a challenging task because of the large number of pedestrians with various poses and appearances appearing at different camera viewpoints. Consequently, various learning approaches have been employed to overcome these challenges. The use of methods that can strike an appropriate balance between speed and accuracy is also a key consideration in this research.Methods: Since one of the key challenges is reducing computational costs, the initial focus is on evaluating various methods. Subsequently, improvements to these methods have been made by adding components to networks that have low computational costs. The most significant of these modifications is the addition of an Image Re-Retrieval Layer (IRL) to the Backbone network to investigate changes in accuracy. Results: Given that increasing computational speed is a fundamental goal of this work, the use of MobileNetV۲ architecture as the Backbone network has been considered. The IRL block has been designed for minimal impact on computational speed. By examining this component, specifically for the CUHK۰۳ dataset, there was a ۵% increase in mAP and a ۳% increase in @Rank۱. For the Market-۱۵۰۱ dataset, the improvement is partially evident. Comparisons with more complex architectures have shown a significant increase in computational speed in these methods.Conclusion: Reducing computational costs while increasing relative recognition accuracy are interdependent objectives. Depending on the specific context and priorities, one might emphasize one over the other when selecting an appropriate method. The changes applied in this research can lead to more optimal results in method selection, striking a balance between computational efficiency and recognition accuracy.

کلیدواژه ها:

Person Re-identification ، Deep Learning ، Convolutional Neural Network ، Image Detection