1,575 research outputs found
Review of Person Re-identification Techniques
Person re-identification across different surveillance cameras with disjoint
fields of view has become one of the most interesting and challenging subjects
in the area of intelligent video surveillance. Although several methods have
been developed and proposed, certain limitations and unresolved issues remain.
In all of the existing re-identification approaches, feature vectors are
extracted from segmented still images or video frames. Different similarity or
dissimilarity measures have been applied to these vectors. Some methods have
used simple constant metrics, whereas others have utilised models to obtain
optimised metrics. Some have created models based on local colour or texture
information, and others have built models based on the gait of people. In
general, the main objective of all these approaches is to achieve a
higher-accuracy rate and lowercomputational costs. This study summarises
several developments in recent literature and discusses the various available
methods used in person re-identification. Specifically, their advantages and
disadvantages are mentioned and compared.Comment: Published 201
Tracking by Prediction: A Deep Generative Model for Mutli-Person localisation and Tracking
Current multi-person localisation and tracking systems have an over reliance
on the use of appearance models for target re-identification and almost no
approaches employ a complete deep learning solution for both objectives. We
present a novel, complete deep learning framework for multi-person localisation
and tracking. In this context we first introduce a light weight sequential
Generative Adversarial Network architecture for person localisation, which
overcomes issues related to occlusions and noisy detections, typically found in
a multi person environment. In the proposed tracking framework we build upon
recent advances in pedestrian trajectory prediction approaches and propose a
novel data association scheme based on predicted trajectories. This removes the
need for computationally expensive person re-identification systems based on
appearance features and generates human like trajectories with minimal
fragmentation. The proposed method is evaluated on multiple public benchmarks
including both static and dynamic cameras and is capable of generating
outstanding performance, especially among other recently proposed deep neural
network based approaches.Comment: To appear in IEEE Winter Conference on Applications of Computer
Vision (WACV), 201
Online Domain Adaptation for Multi-Object Tracking
Automatically detecting, labeling, and tracking objects in videos depends
first and foremost on accurate category-level object detectors. These might,
however, not always be available in practice, as acquiring high-quality large
scale labeled training datasets is either too costly or impractical for all
possible real-world application scenarios. A scalable solution consists in
re-using object detectors pre-trained on generic datasets. This work is the
first to investigate the problem of on-line domain adaptation of object
detectors for causal multi-object tracking (MOT). We propose to alleviate the
dataset bias by adapting detectors from category to instances, and back: (i) we
jointly learn all target models by adapting them from the pre-trained one, and
(ii) we also adapt the pre-trained model on-line. We introduce an on-line
multi-task learning algorithm to efficiently share parameters and reduce drift,
while gradually improving recall. Our approach is applicable to any linear
object detector, and we evaluate both cheap "mini-Fisher Vectors" and expensive
"off-the-shelf" ConvNet features. We quantitatively measure the benefit of our
domain adaptation strategy on the KITTI tracking benchmark and on a new dataset
(PASCAL-to-KITTI) we introduce to study the domain mismatch problem in MOT.Comment: To appear at BMVC 201
Simple Online and Realtime Tracking with a Deep Association Metric
Simple Online and Realtime Tracking (SORT) is a pragmatic approach to
multiple object tracking with a focus on simple, effective algorithms. In this
paper, we integrate appearance information to improve the performance of SORT.
Due to this extension we are able to track objects through longer periods of
occlusions, effectively reducing the number of identity switches. In spirit of
the original framework we place much of the computational complexity into an
offline pre-training stage where we learn a deep association metric on a
large-scale person re-identification dataset. During online application, we
establish measurement-to-track associations using nearest neighbor queries in
visual appearance space. Experimental evaluation shows that our extensions
reduce the number of identity switches by 45%, achieving overall competitive
performance at high frame rates.Comment: 5 pages, 1 figur
Enhanced tracking and recognition of moving objects by reasoning about spatio-temporal continuity.
A framework for the logical and statistical analysis and annotation of dynamic scenes containing occlusion and other uncertainties is presented. This framework consists
of three elements; an object tracker module, an object recognition/classification module and a logical consistency, ambiguity and error reasoning engine. The principle behind the object tracker and object recognition modules is to reduce error by increasing ambiguity (by merging objects in close proximity and presenting multiple
hypotheses). The reasoning engine deals with error, ambiguity and occlusion in a unified framework to produce a hypothesis that satisfies fundamental constraints
on the spatio-temporal continuity of objects. Our algorithm finds a globally consistent model of an extended video sequence that is maximally supported by a voting function based on the output of a statistical classifier. The system results
in an annotation that is significantly more accurate than what would be obtained
by frame-by-frame evaluation of the classifier output. The framework has been implemented
and applied successfully to the analysis of team sports with a single
camera.
Key words: Visua
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