2,331 research outputs found
A Deep Four-Stream Siamese Convolutional Neural Network with Joint Verification and Identification Loss for Person Re-detection
State-of-the-art person re-identification systems that employ a triplet based
deep network suffer from a poor generalization capability. In this paper, we
propose a four stream Siamese deep convolutional neural network for person
redetection that jointly optimises verification and identification losses over
a four image input group. Specifically, the proposed method overcomes the
weakness of the typical triplet formulation by using groups of four images
featuring two matched (i.e. the same identity) and two mismatched images. This
allows us to jointly increase the interclass variations and reduce the
intra-class variations in the learned feature space. The proposed approach also
optimises over both the identification and verification losses, further
minimising intra-class variation and maximising inter-class variation,
improving overall performance. Extensive experiments on four challenging
datasets, VIPeR, CUHK01, CUHK03 and PRID2011, demonstrates that the proposed
approach achieves state-of-the-art performance.Comment: Published in WACV 201
Query-guided End-to-End Person Search
Person search has recently gained attention as the novel task of finding a
person, provided as a cropped sample, from a gallery of non-cropped images,
whereby several other people are also visible. We believe that i. person
detection and re-identification should be pursued in a joint optimization
framework and that ii. the person search should leverage the query image
extensively (e.g. emphasizing unique query patterns). However, so far, no prior
art realizes this. We introduce a novel query-guided end-to-end person search
network (QEEPS) to address both aspects. We leverage a most recent joint
detector and re-identification work, OIM [37]. We extend this with i. a
query-guided Siamese squeeze-and-excitation network (QSSE-Net) that uses global
context from both the query and gallery images, ii. a query-guided region
proposal network (QRPN) to produce query-relevant proposals, and iii. a
query-guided similarity subnetwork (QSimNet), to learn a query-guided
reidentification score. QEEPS is the first end-to-end query-guided detection
and re-id network. On both the most recent CUHK-SYSU [37] and PRW [46]
datasets, we outperform the previous state-of-the-art by a large margin.Comment: Accepted as poster in CVPR 201
CIAGAN: Conditional Identity Anonymization Generative Adversarial Networks
The unprecedented increase in the usage of computer vision technology in
society goes hand in hand with an increased concern in data privacy. In many
real-world scenarios like people tracking or action recognition, it is
important to be able to process the data while taking careful consideration in
protecting people's identity. We propose and develop CIAGAN, a model for image
and video anonymization based on conditional generative adversarial networks.
Our model is able to remove the identifying characteristics of faces and bodies
while producing high-quality images and videos that can be used for any
computer vision task, such as detection or tracking. Unlike previous methods,
we have full control over the de-identification (anonymization) procedure,
ensuring both anonymization as well as diversity. We compare our method to
several baselines and achieve state-of-the-art results.Comment: CVPR 202
- …