132 research outputs found
Exploiting feature representations through similarity learning, post-ranking and ranking aggregation for person re-identification
Person re-identification has received special attention by the human analysis
community in the last few years. To address the challenges in this field, many
researchers have proposed different strategies, which basically exploit either
cross-view invariant features or cross-view robust metrics. In this work, we
propose to exploit a post-ranking approach and combine different feature
representations through ranking aggregation. Spatial information, which
potentially benefits the person matching, is represented using a 2D body model,
from which color and texture information are extracted and combined. We also
consider background/foreground information, automatically extracted via Deep
Decompositional Network, and the usage of Convolutional Neural Network (CNN)
features. To describe the matching between images we use the polynomial feature
map, also taking into account local and global information. The Discriminant
Context Information Analysis based post-ranking approach is used to improve
initial ranking lists. Finally, the Stuart ranking aggregation method is
employed to combine complementary ranking lists obtained from different feature
representations. Experimental results demonstrated that we improve the
state-of-the-art on VIPeR and PRID450s datasets, achieving 67.21% and 75.64% on
top-1 rank recognition rate, respectively, as well as obtaining competitive
results on CUHK01 dataset.Comment: Preprint submitted to Image and Vision Computin
Multi-shot Pedestrian Re-identification via Sequential Decision Making
Multi-shot pedestrian re-identification problem is at the core of
surveillance video analysis. It matches two tracks of pedestrians from
different cameras. In contrary to existing works that aggregate single frames
features by time series model such as recurrent neural network, in this paper,
we propose an interpretable reinforcement learning based approach to this
problem. Particularly, we train an agent to verify a pair of images at each
time. The agent could choose to output the result (same or different) or
request another pair of images to verify (unsure). By this way, our model
implicitly learns the difficulty of image pairs, and postpone the decision when
the model does not accumulate enough evidence. Moreover, by adjusting the
reward for unsure action, we can easily trade off between speed and accuracy.
In three open benchmarks, our method are competitive with the state-of-the-art
methods while only using 3% to 6% images. These promising results demonstrate
that our method is favorable in both efficiency and performance
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
Learning Deep Context-aware Features over Body and Latent Parts for Person Re-identification
Person Re-identification (ReID) is to identify the same person across
different cameras. It is a challenging task due to the large variations in
person pose, occlusion, background clutter, etc How to extract powerful
features is a fundamental problem in ReID and is still an open problem today.
In this paper, we design a Multi-Scale Context-Aware Network (MSCAN) to learn
powerful features over full body and body parts, which can well capture the
local context knowledge by stacking multi-scale convolutions in each layer.
Moreover, instead of using predefined rigid parts, we propose to learn and
localize deformable pedestrian parts using Spatial Transformer Networks (STN)
with novel spatial constraints. The learned body parts can release some
difficulties, eg pose variations and background clutters, in part-based
representation. Finally, we integrate the representation learning processes of
full body and body parts into a unified framework for person ReID through
multi-class person identification tasks. Extensive evaluations on current
challenging large-scale person ReID datasets, including the image-based
Market1501, CUHK03 and sequence-based MARS datasets, show that the proposed
method achieves the state-of-the-art results.Comment: Accepted by CVPR 201
STA: Spatial-Temporal Attention for Large-Scale Video-based Person Re-Identification
In this work, we propose a novel Spatial-Temporal Attention (STA) approach to
tackle the large-scale person re-identification task in videos. Different from
the most existing methods, which simply compute representations of video clips
using frame-level aggregation (e.g. average pooling), the proposed STA adopts a
more effective way for producing robust clip-level feature representation.
Concretely, our STA fully exploits those discriminative parts of one target
person in both spatial and temporal dimensions, which results in a 2-D
attention score matrix via inter-frame regularization to measure the
importances of spatial parts across different frames. Thus, a more robust
clip-level feature representation can be generated according to a weighted sum
operation guided by the mined 2-D attention score matrix. In this way, the
challenging cases for video-based person re-identification such as pose
variation and partial occlusion can be well tackled by the STA. We conduct
extensive experiments on two large-scale benchmarks, i.e. MARS and
DukeMTMC-VideoReID. In particular, the mAP reaches 87.7% on MARS, which
significantly outperforms the state-of-the-arts with a large margin of more
than 11.6%.Comment: Accepted as a conference paper at AAAI 201
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