18,523 research outputs found
Online learning on incremental distance metric for person re-identification
Person re-identification is to match persons appearing across non-overlapping cameras. The matching is challenging due to visual ambiguities and disparities of human bodies. Most previous distance metrics are learned by off-line and supervised approaches. However, they are not practical in real-world applications in which online data comes in without any label. In this paper, a novel online learning approach on incremental distance metric, OL-IDM, is proposed. The approach firstly modifies Self-Organizing Incremental Neural Network (SOINN) using Mahalanobis distance metric to cluster incoming data into neural nodes. Such metric maximizes the likelihood of a true image pair matches with a smaller distance than that of a wrong matched pair. Second, an algorithm for construction of incremental training sets is put forward. Then a distance metric learning algorithm called Keep It Simple and Straightforward Metric (KISSME) trains on the incremental training sets in order to obtain a better distance metric for the neural network. Aforesaid procedures are validated on three large person re-identification datasets and experimental results show the proposed approach's competitive performance to state-of-the-art supervised methods and self-adaption to real-world data. ? 2014 IEEE.EI1421-142
Temporal Model Adaptation for Person Re-Identification
Person re-identification is an open and challenging problem in computer
vision. Majority of the efforts have been spent either to design the best
feature representation or to learn the optimal matching metric. Most approaches
have neglected the problem of adapting the selected features or the learned
model over time. To address such a problem, we propose a temporal model
adaptation scheme with human in the loop. We first introduce a
similarity-dissimilarity learning method which can be trained in an incremental
fashion by means of a stochastic alternating directions methods of multipliers
optimization procedure. Then, to achieve temporal adaptation with limited human
effort, we exploit a graph-based approach to present the user only the most
informative probe-gallery matches that should be used to update the model.
Results on three datasets have shown that our approach performs on par or even
better than state-of-the-art approaches while reducing the manual pairwise
labeling effort by about 80%
Memory Based Online Learning of Deep Representations from Video Streams
We present a novel online unsupervised method for face identity learning from
video streams. The method exploits deep face descriptors together with a memory
based learning mechanism that takes advantage of the temporal coherence of
visual data. Specifically, we introduce a discriminative feature matching
solution based on Reverse Nearest Neighbour and a feature forgetting strategy
that detect redundant features and discard them appropriately while time
progresses. It is shown that the proposed learning procedure is asymptotically
stable and can be effectively used in relevant applications like multiple face
identification and tracking from unconstrained video streams. Experimental
results show that the proposed method achieves comparable results in the task
of multiple face tracking and better performance in face identification with
offline approaches exploiting future information. Code will be publicly
available.Comment: arXiv admin note: text overlap with arXiv:1708.0361
Highly Efficient Regression for Scalable Person Re-Identification
Existing person re-identification models are poor for scaling up to large
data required in real-world applications due to: (1) Complexity: They employ
complex models for optimal performance resulting in high computational cost for
training at a large scale; (2) Inadaptability: Once trained, they are
unsuitable for incremental update to incorporate any new data available. This
work proposes a truly scalable solution to re-id by addressing both problems.
Specifically, a Highly Efficient Regression (HER) model is formulated by
embedding the Fisher's criterion to a ridge regression model for very fast
re-id model learning with scalable memory/storage usage. Importantly, this new
HER model supports faster than real-time incremental model updates therefore
making real-time active learning feasible in re-id with human-in-the-loop.
Extensive experiments show that such a simple and fast model not only
outperforms notably the state-of-the-art re-id methods, but also is more
scalable to large data with additional benefits to active learning for reducing
human labelling effort in re-id deployment
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