30,828 research outputs found
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
A Causal And-Or Graph Model for Visibility Fluent Reasoning in Tracking Interacting Objects
Tracking humans that are interacting with the other subjects or environment
remains unsolved in visual tracking, because the visibility of the human of
interests in videos is unknown and might vary over time. In particular, it is
still difficult for state-of-the-art human trackers to recover complete human
trajectories in crowded scenes with frequent human interactions. In this work,
we consider the visibility status of a subject as a fluent variable, whose
change is mostly attributed to the subject's interaction with the surrounding,
e.g., crossing behind another object, entering a building, or getting into a
vehicle, etc. We introduce a Causal And-Or Graph (C-AOG) to represent the
causal-effect relations between an object's visibility fluent and its
activities, and develop a probabilistic graph model to jointly reason the
visibility fluent change (e.g., from visible to invisible) and track humans in
videos. We formulate this joint task as an iterative search of a feasible
causal graph structure that enables fast search algorithm, e.g., dynamic
programming method. We apply the proposed method on challenging video sequences
to evaluate its capabilities of estimating visibility fluent changes of
subjects and tracking subjects of interests over time. Results with comparisons
demonstrate that our method outperforms the alternative trackers and can
recover complete trajectories of humans in complicated scenarios with frequent
human interactions.Comment: accepted by CVPR 201
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