Keypoint Based Feature Matching For Partial Person Re Identification

Figure 3 From Keypoint Based Feature Matching For Partial Person Since we cannot locate the part of the partial image, the misaligned region compromises the performance greatly when directly (a) compare a partial pedestrian with a holistic one. to alleviate this issue, we propose a keypoint based feature matching (kbfm) network, which constructs a simple and effective framework for partial re id. Welcome to the official repository of our eccv24 paper "keypoint promptable re identification". in this work, we propose kpr, a keypoint promptable method for part based person re identification. kpr is a swin transformer based model that takes an rgb image along with a set of semantic keypoints as input (i.e. the prompt).

Figure 1 From Keypoint Based Feature Matching For Partial Person A keypoint based feature matching (kbfm) network is proposed, which constructs a simple and effective framework for partial re id and explicitly leverages the keypoints generated by pose estimation. as a derivative of person re identification (re id), partial re id aims to retrieve a partial pedestrian across holistic person images captured by. Request pdf | on oct 1, 2020, chuchu han and others published keypoint based feature matching for partial person re identification | find, read and cite all the research you need on researchgate. Fig. 3. diagram of the improved triplet loss. there are p identities in a batch and each identity contains 1 holistic pedestrian andk − 1 partial ones. for a partial anchor, its positive sample is the holistic one with the same identity, and the negative sample is the hardest one from the rest p − 1 holistic samples. keypoint selection is performed before triplet construction, which means. Keypoint based feature matching for partial person re identification chuchu han, changxin gao, nong sang. sps. members: free.