42 research outputs found
Unsupervised Adaptive Re-identification in Open World Dynamic Camera Networks
Person re-identification is an open and challenging problem in computer
vision. Existing approaches have concentrated on either designing the best
feature representation or learning optimal matching metrics in a static setting
where the number of cameras are fixed in a network. Most approaches have
neglected the dynamic and open world nature of the re-identification problem,
where a new camera may be temporarily inserted into an existing system to get
additional information. To address such a novel and very practical problem, we
propose an unsupervised adaptation scheme for re-identification models in a
dynamic camera network. First, we formulate a domain perceptive
re-identification method based on geodesic flow kernel that can effectively
find the best source camera (already installed) to adapt with a newly
introduced target camera, without requiring a very expensive training phase.
Second, we introduce a transitive inference algorithm for re-identification
that can exploit the information from best source camera to improve the
accuracy across other camera pairs in a network of multiple cameras. Extensive
experiments on four benchmark datasets demonstrate that the proposed approach
significantly outperforms the state-of-the-art unsupervised learning based
alternatives whilst being extremely efficient to compute.Comment: CVPR 2017 Spotligh
Clip-level feature aggregation : a key factor for video-based person re-identification
In the task of video-based person re-identification, features
of persons in the query and gallery sets are compared to search the
best match. Generally, most existing methods aggregate the frame-level
features together using a temporal method to generate the clip-level fea-
tures, instead of the sequence-level representations. In this paper, we
propose a new method that aggregates the clip-level features to obtain
the sequence-level representations of persons, which consists of two parts,
i.e., Average Aggregation Strategy (AAS) and Raw Feature Utilization
(RFU). AAS makes use of all frames in a video sequence to generate
a better representation of a person, while RFU investigates how batch
normalization operation influences feature representations in person re-
identification. The experimental results demonstrate that our method
can boost the performance of existing models for better accuracy. In
particular, we achieve 87.7% rank-1 and 82.3% mAP on MARS dataset
without any post-processing procedure, which outperforms the existing
state-of-the-art