25,880 research outputs found
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
Deep Adaptive Feature Embedding with Local Sample Distributions for Person Re-identification
Person re-identification (re-id) aims to match pedestrians observed by
disjoint camera views. It attracts increasing attention in computer vision due
to its importance to surveillance system. To combat the major challenge of
cross-view visual variations, deep embedding approaches are proposed by
learning a compact feature space from images such that the Euclidean distances
correspond to their cross-view similarity metric. However, the global Euclidean
distance cannot faithfully characterize the ideal similarity in a complex
visual feature space because features of pedestrian images exhibit unknown
distributions due to large variations in poses, illumination and occlusion.
Moreover, intra-personal training samples within a local range are robust to
guide deep embedding against uncontrolled variations, which however, cannot be
captured by a global Euclidean distance. In this paper, we study the problem of
person re-id by proposing a novel sampling to mine suitable \textit{positives}
(i.e. intra-class) within a local range to improve the deep embedding in the
context of large intra-class variations. Our method is capable of learning a
deep similarity metric adaptive to local sample structure by minimizing each
sample's local distances while propagating through the relationship between
samples to attain the whole intra-class minimization. To this end, a novel
objective function is proposed to jointly optimize similarity metric learning,
local positive mining and robust deep embedding. This yields local
discriminations by selecting local-ranged positive samples, and the learned
features are robust to dramatic intra-class variations. Experiments on
benchmarks show state-of-the-art results achieved by our method.Comment: Published on Pattern Recognitio
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
- …