28 research outputs found
Divide and Fuse: A Re-ranking Approach for Person Re-identification
As re-ranking is a necessary procedure to boost person re-identification
(re-ID) performance on large-scale datasets, the diversity of feature becomes
crucial to person reID for its importance both on designing pedestrian
descriptions and re-ranking based on feature fusion. However, in many
circumstances, only one type of pedestrian feature is available. In this paper,
we propose a "Divide and use" re-ranking framework for person re-ID. It
exploits the diversity from different parts of a high-dimensional feature
vector for fusion-based re-ranking, while no other features are accessible.
Specifically, given an image, the extracted feature is divided into
sub-features. Then the contextual information of each sub-feature is
iteratively encoded into a new feature. Finally, the new features from the same
image are fused into one vector for re-ranking. Experimental results on two
person re-ID benchmarks demonstrate the effectiveness of the proposed
framework. Especially, our method outperforms the state-of-the-art on the
Market-1501 dataset.Comment: Accepted by BMVC201
A Pose-Sensitive Embedding for Person Re-Identification with Expanded Cross Neighborhood Re-Ranking
Person re identification is a challenging retrieval task that requires
matching a person's acquired image across non overlapping camera views. In this
paper we propose an effective approach that incorporates both the fine and
coarse pose information of the person to learn a discriminative embedding. In
contrast to the recent direction of explicitly modeling body parts or
correcting for misalignment based on these, we show that a rather
straightforward inclusion of acquired camera view and/or the detected joint
locations into a convolutional neural network helps to learn a very effective
representation. To increase retrieval performance, re-ranking techniques based
on computed distances have recently gained much attention. We propose a new
unsupervised and automatic re-ranking framework that achieves state-of-the-art
re-ranking performance. We show that in contrast to the current
state-of-the-art re-ranking methods our approach does not require to compute
new rank lists for each image pair (e.g., based on reciprocal neighbors) and
performs well by using simple direct rank list based comparison or even by just
using the already computed euclidean distances between the images. We show that
both our learned representation and our re-ranking method achieve
state-of-the-art performance on a number of challenging surveillance image and
video datasets.
The code is available online at:
https://github.com/pse-ecn/pose-sensitive-embeddingComment: CVPR 2018: v2 (fixes, added new results on PRW dataset
Person Re-identification with Deep Similarity-Guided Graph Neural Network
The person re-identification task requires to robustly estimate visual
similarities between person images. However, existing person re-identification
models mostly estimate the similarities of different image pairs of probe and
gallery images independently while ignores the relationship information between
different probe-gallery pairs. As a result, the similarity estimation of some
hard samples might not be accurate. In this paper, we propose a novel deep
learning framework, named Similarity-Guided Graph Neural Network (SGGNN) to
overcome such limitations. Given a probe image and several gallery images,
SGGNN creates a graph to represent the pairwise relationships between
probe-gallery pairs (nodes) and utilizes such relationships to update the
probe-gallery relation features in an end-to-end manner. Accurate similarity
estimation can be achieved by using such updated probe-gallery relation
features for prediction. The input features for nodes on the graph are the
relation features of different probe-gallery image pairs. The probe-gallery
relation feature updating is then performed by the messages passing in SGGNN,
which takes other nodes' information into account for similarity estimation.
Different from conventional GNN approaches, SGGNN learns the edge weights with
rich labels of gallery instance pairs directly, which provides relation fusion
more precise information. The effectiveness of our proposed method is validated
on three public person re-identification datasets.Comment: accepted to ECCV 201
Video-based Visible-Infrared Person Re-Identification with Auxiliary Samples
Visible-infrared person re-identification (VI-ReID) aims to match persons
captured by visible and infrared cameras, allowing person retrieval and
tracking in 24-hour surveillance systems. Previous methods focus on learning
from cross-modality person images in different cameras. However, temporal
information and single-camera samples tend to be neglected. To crack this nut,
in this paper, we first contribute a large-scale VI-ReID dataset named
BUPTCampus. Different from most existing VI-ReID datasets, it 1) collects
tracklets instead of images to introduce rich temporal information, 2) contains
pixel-aligned cross-modality sample pairs for better modality-invariant
learning, 3) provides one auxiliary set to help enhance the optimization, in
which each identity only appears in a single camera. Based on our constructed
dataset, we present a two-stream framework as baseline and apply Generative
Adversarial Network (GAN) to narrow the gap between the two modalities. To
exploit the advantages introduced by the auxiliary set, we propose a curriculum
learning based strategy to jointly learn from both primary and auxiliary sets.
Moreover, we design a novel temporal k-reciprocal re-ranking method to refine
the ranking list with fine-grained temporal correlation cues. Experimental
results demonstrate the effectiveness of the proposed methods. We also
reproduce 9 state-of-the-art image-based and video-based VI-ReID methods on
BUPTCampus and our methods show substantial superiority to them. The codes and
dataset are available at: https://github.com/dyhBUPT/BUPTCampus.Comment: Accepted by Transactions on Information Forensics & Security 202