This article illustrates the application of the Kalman filter in real-time human re-identification tasks to improve accuracy, reliability, and reduce computational costs while determining a person's position and orientation. The use of the Kalman filter addresses noise filtering and prediction issues in human re-identification tasks.
The study aims to explore a method for identifying corresponding objects across multiple camera views, to improve the accuracy of object re-identification. We analyzed various techniques, including contour detection, region of interest extraction, and keypoint extraction. We also examined the challenges of finding object correspondences between multiple camera views. To evaluate the effectiveness of the proposed method, we utilized two human attribute datasets, Market-1501 and DukeMTMC-reID, and performed extensive testing on these datasets.
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