TY - GEN
T1 - What makes you look like you
T2 - 16th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2019
AU - Wei, Wen Li
AU - Lin, Jen Chun
AU - Lin, Yen Yu
AU - Liao, Hong Yuan Mark
PY - 2019/9
Y1 - 2019/9
N2 - In this work, we address person re-identification (ReID) by learning an inherent feature representation (inherent code) that is unique to each individual. This task is difficult because the appearance of a person may vary dramatically due to diverse factors, such as illuminations, viewpoints, and human pose changes. To tackle this issue, we propose new learning objectives to learn the inherent code for each person based on deep learning. Specifically, the proposed deep-net model is trained by jointly optimizing the multiple objectives that pulls the instances of the same person closer while pushing the instances belonging to different persons far from each other. Owing to such complementary designs, the deep-net model yields a robust code for each individual and hence better solve person ReID. Promising experimental results demonstrate the robustness and effectiveness of our proposed method.
AB - In this work, we address person re-identification (ReID) by learning an inherent feature representation (inherent code) that is unique to each individual. This task is difficult because the appearance of a person may vary dramatically due to diverse factors, such as illuminations, viewpoints, and human pose changes. To tackle this issue, we propose new learning objectives to learn the inherent code for each person based on deep learning. Specifically, the proposed deep-net model is trained by jointly optimizing the multiple objectives that pulls the instances of the same person closer while pushing the instances belonging to different persons far from each other. Owing to such complementary designs, the deep-net model yields a robust code for each individual and hence better solve person ReID. Promising experimental results demonstrate the robustness and effectiveness of our proposed method.
UR - http://www.scopus.com/inward/record.url?scp=85076353773&partnerID=8YFLogxK
U2 - 10.1109/AVSS.2019.8909892
DO - 10.1109/AVSS.2019.8909892
M3 - Conference contribution
AN - SCOPUS:85076353773
T3 - 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2019
BT - 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2019
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 18 September 2019 through 21 September 2019
ER -