Nhận diện lại người một phần bằng cách sử dụng mạng điều chỉnh tư thế với học tập mặt nạ

Springer Science and Business Media LLC - Tập 52 - Trang 10885-10900 - 2022
Qilu Qiu1, Jieyu Zhao1, Ye Zheng1
1Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, China

Tóm tắt

Nhận diện lại người một phần là một nhiệm vụ thách thức, trong đó chỉ có thể quan sát một phần của người đó. Việc so sánh trực tiếp một hình ảnh một phần với hình ảnh toàn diện dẫn đến sự không phù hợp nghiêm trọng, làm giảm hiệu suất của các thuật toán nhận diện lại. Trong bài báo này, chúng tôi đề xuất một mạng điều chỉnh tư thế và học tập mặt nạ (PMN) để giải quyết các vấn đề về sự thiếu hụt lớn các phần và sự không phù hợp đáng kể của người đi bộ. Mô hình được đề xuất bao gồm một mô-đun biến hình không gian theo tư thế (PST) và một bộ trích xuất đặc trưng có mặt nạ. Mô-đun PST lấy mẫu một hình ảnh được biến đổi afine từ hình ảnh toàn diện/ một phần để căn chỉnh hình ảnh người đi bộ với tư thế chuẩn. Bộ trích xuất đặc trưng có mặt nạ, bao gồm một mạng lưới nền tảng và một nhánh học tập mặt nạ (MLB), được thiết kế để học tính khả thi của các phần cơ thể nhằm chọn lọc các đặc trưng hiệu quả. Các kết quả thực nghiệm trên hai bộ điểm chuẩn nhận diện người một phần được báo cáo cho thấy phương pháp được đề xuất đạt hiệu suất cạnh tranh so với các phương pháp tiên tiến nhất.

Từ khóa

#nhận diện lại người #điều chỉnh tư thế #học tập mặt nạ #mạng điều chỉnh tư thế

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