96,766 research outputs found
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
Adaptation of Person Re-identification Models for On-boarding New Camera(s)
Existing approaches for person re-identification 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 one or multiple new cameras may be temporarily on-boarded into an ex- isting system to get additional information or added to expand an existing network. To address such a very practical problem, we propose a novel approach for adapting existing multi-camera re-identification frameworks with limited supervision. 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 newly introduced target camera(s), 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. Third, we develop a target-aware sparse prototype selection strategy for finding an informative subset of source camera data for data-efficient learning in resource constrained environments. Our approach can greatly increase the flexibility and reduce the deployment cost of new cameras in many real-world dy- namic camera networks. Extensive experiments demonstrate that our approach significantly outperforms state-of-the-art unsupervised alternatives whilst being extremely efficient to compute
Camera On-boarding for Person Re-identification using Hypothesis Transfer Learning
Most of the existing approaches for person re-identification consider a
static setting where the number of cameras in the network is fixed. An
interesting direction, which has received little attention, is to explore the
dynamic nature of a camera network, where one tries to adapt the existing
re-identification models after on-boarding new cameras, with little additional
effort. There have been a few recent methods proposed in person
re-identification that attempt to address this problem by assuming the labeled
data in the existing network is still available while adding new cameras. This
is a strong assumption since there may exist some privacy issues for which one
may not have access to those data. Rather, based on the fact that it is easy to
store the learned re-identifications models, which mitigates any data privacy
concern, we develop an efficient model adaptation approach using hypothesis
transfer learning that aims to transfer the knowledge using only source models
and limited labeled data, but without using any source camera data from the
existing network. Our approach minimizes the effect of negative transfer by
finding an optimal weighted combination of multiple source models for
transferring the knowledge. Extensive experiments on four challenging benchmark
datasets with a variable number of cameras well demonstrate the efficacy of our
proposed approach over state-of-the-art methods.Comment: Accepted to CVPR 202
Complete Solution for Vehicle Re-ID in Surround-view Camera System
Vehicle re-identification (Re-ID) is a critical component of the autonomous
driving perception system, and research in this area has accelerated in recent
years. However, there is yet no perfect solution to the vehicle
re-identification issue associated with the car's surround-view camera system.
Our analysis identifies two significant issues in the aforementioned scenario:
i) It is difficult to identify the same vehicle in many picture frames due to
the unique construction of the fisheye camera. ii) The appearance of the same
vehicle when seen via the surround vision system's several cameras is rather
different. To overcome these issues, we suggest an integrative vehicle Re-ID
solution method. On the one hand, we provide a technique for determining the
consistency of the tracking box drift with respect to the target. On the other
hand, we combine a Re-ID network based on the attention mechanism with spatial
limitations to increase performance in situations involving multiple cameras.
Finally, our approach combines state-of-the-art accuracy with real-time
performance. We will soon make the source code and annotated fisheye dataset
available.Comment: 11 pages, 10 figures. arXiv admin note: substantial text overlap with
arXiv:2006.1650
Temporal Continuity Based Unsupervised Learning for Person Re-Identification
Person re-identification (re-id) aims to match the same person from images
taken across multiple cameras. Most existing person re-id methods generally
require a large amount of identity labeled data to act as discriminative
guideline for representation learning. Difficulty in manually collecting
identity labeled data leads to poor adaptability in practical scenarios. To
overcome this problem, we propose an unsupervised center-based clustering
approach capable of progressively learning and exploiting the underlying re-id
discriminative information from temporal continuity within a camera. We call
our framework Temporal Continuity based Unsupervised Learning (TCUL).
Specifically, TCUL simultaneously does center based clustering of unlabeled
(target) dataset and fine-tunes a convolutional neural network (CNN)
pre-trained on irrelevant labeled (source) dataset to enhance discriminative
capability of the CNN for the target dataset. Furthermore, it exploits
temporally continuous nature of images within-camera jointly with spatial
similarity of feature maps across-cameras to generate reliable pseudo-labels
for training a re-identification model. As the training progresses, number of
reliable samples keep on growing adaptively which in turn boosts representation
ability of the CNN. Extensive experiments on three large-scale person re-id
benchmark datasets are conducted to compare our framework with state-of-the-art
techniques, which demonstrate superiority of TCUL over existing methods
People tracking in a smart campus context using multiple cameras
Object multi-tracking has been a relevant topic for different applications, such as surveillance, mobility, and ambient intelligence. It is particularly challenging when considering open spaces, like Smart Cities, which demand multi-camera solutions with issues like re-identification. In this paper, we describe a framework aiming to provide multi-tracking of people throughout a university campus as part of a larger project (Lab4USpaces) to develop a Smart Campus initiative. Several object detection models and real-time tracking open-source algorithms were compared. The project contemplates a set of low-cost video cameras covering most of the campus, with or without overlapping. After researching different alternatives, the proposed framework uses the YOLOv7 tiny model for object detection, BoT-Sort for multiple object tracking, and Deep Person Reid for re-identification. We also faced challenges concerning the privacy and security of campus users. The multi-tracking system complies with current regulations since no personal identification is ever performed, and no images are stored for longer than necessary for object detection and re-identification. Besides describing the first prototype, this paper discusses some validation tests and describes some potential uses.- (undefined
The Caltech Fish Counting Dataset: A Benchmark for Multiple-Object Tracking and Counting
We present the Caltech Fish Counting Dataset (CFC), a large-scale dataset for
detecting, tracking, and counting fish in sonar videos. We identify sonar
videos as a rich source of data for advancing low signal-to-noise computer
vision applications and tackling domain generalization in multiple-object
tracking (MOT) and counting. In comparison to existing MOT and counting
datasets, which are largely restricted to videos of people and vehicles in
cities, CFC is sourced from a natural-world domain where targets are not easily
resolvable and appearance features cannot be easily leveraged for target
re-identification. With over half a million annotations in over 1,500 videos
sourced from seven different sonar cameras, CFC allows researchers to train MOT
and counting algorithms and evaluate generalization performance at unseen test
locations. We perform extensive baseline experiments and identify key
challenges and opportunities for advancing the state of the art in
generalization in MOT and counting.Comment: ECCV 2022. 33 pages, 12 figure
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