사람 재인식을 위한 개선된 PersonNet
Advanced PersonNet for Person Re-Identification
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참고문헌
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초록
이 논문에서는 사람 재식별 모델인 PersonNet의 성능을 개선하는 방법을 제안하고 실험한다. 특징점 추출을 위해 인셉션레이어를 접목하여, 기존 32개의 특징점을 154개로 증가시켜 강화하였다. 또한, PersonNet에서 사용하는 CND 방식을 수정하여 비대칭성을 완화하였고, 보행자 이미지의 특징점을 3부분으로 나누어 가중치를 적용한 방법을 적용하여 특징을 더 뚜렷하게 파악하도록 하였다. 성능 평가를 위해 CUHK01, CUHK03 그리고 Market-1501 3가지의 데이터베이스를 사용하였고 실험결과 27～31% 성능이 개선되었다.
Abstract
This paper propose and experiment advanced PersonNet, a human identification model, with advanced performance. We apply the inception layer to extract feature points, and increase the existing 32 feature points to 154. Also, wemodify the CND method used by PersonNet to mitigate asymmetry, and apply weights to the feature map ofpedestrian images in three parts, thereby making the features more distinct. Three databases were used forperformance evaluation：CUHK01, CUHK03 and Market-1501. The experiment results showed 27-31% improvement inperformance.
참고문헌 (13)
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