17,223 research outputs found
Fine-grained Domain Adaptive Crowd Counting via Point-derived Segmentation
Due to domain shift, a large performance drop is usually observed when a
trained crowd counting model is deployed in the wild. While existing
domain-adaptive crowd counting methods achieve promising results, they
typically regard each crowd image as a whole and reduce domain discrepancies in
a holistic manner, thus limiting further improvement of domain adaptation
performance. To this end, we propose to untangle \emph{domain-invariant} crowd
and \emph{domain-specific} background from crowd images and design a
fine-grained domain adaption method for crowd counting. Specifically, to
disentangle crowd from background, we propose to learn crowd segmentation from
point-level crowd counting annotations in a weakly-supervised manner. Based on
the derived segmentation, we design a crowd-aware domain adaptation mechanism
consisting of two crowd-aware adaptation modules, i.e., Crowd Region Transfer
(CRT) and Crowd Density Alignment (CDA). The CRT module is designed to guide
crowd features transfer across domains beyond background distractions. The CDA
module dedicates to regularising target-domain crowd density generation by its
own crowd density distribution. Our method outperforms previous approaches
consistently in the widely-used adaptation scenarios.Comment: 10 pages, 5 figures, and 9 table
Crowd behaviour and congestion analysis through deep machine learning
This thesis looks to advance understanding in the field of computer vision based crowd analysis through a combination of deep learning techniques, multi-task learning, and domain adaptation. Issues that have limited progress in this field to date include visual occlusion, scale and perspective issues, variation in scene content as well as a lack of labelled training data. Another negative trend that has emerged in this field as well as in computer vision in general is the development of bespoke, single-task techniques that cannot be easily extended or re-used.
The core contributions of this work are as follows. First, deep learning methods are developed for several crowd analysis tasks including crowd counting, crowd density level estimation, crowd behaviour recognition and crowd behaviour anomaly detection. The proposed data-driven methods are shown to be superior to techniques which rely on hand-crafted features, overcoming many of the observed challenges and achieving state-of-the-art results. Second, multi-task learning strategies are applied to crowd behaviour and congestion analysis tasks, increasing the overall predictive performance and removing redundant model parameters. Finally, domain adaptation techniques are investigated as a means to extend a given crowd analysis model to perform the same task in new visual domains (e.g. medical, wildlife) and vice-versa, with original domain performance preserved
People, Penguins and Petri Dishes: Adapting Object Counting Models To New Visual Domains And Object Types Without Forgetting
In this paper we propose a technique to adapt a convolutional neural network
(CNN) based object counter to additional visual domains and object types while
still preserving the original counting function. Domain-specific normalisation
and scaling operators are trained to allow the model to adjust to the
statistical distributions of the various visual domains. The developed
adaptation technique is used to produce a singular patch-based counting
regressor capable of counting various object types including people, vehicles,
cell nuclei and wildlife. As part of this study a challenging new cell counting
dataset in the context of tissue culture and patient diagnosis is constructed.
This new collection, referred to as the Dublin Cell Counting (DCC) dataset, is
the first of its kind to be made available to the wider computer vision
community. State-of-the-art object counting performance is achieved in both the
Shanghaitech (parts A and B) and Penguins datasets while competitive
performance is observed on the TRANCOS and Modified Bone Marrow (MBM) datasets,
all using a shared counting model.Comment: 10 page
Class-Agnostic Counting
Nearly all existing counting methods are designed for a specific object
class. Our work, however, aims to create a counting model able to count any
class of object. To achieve this goal, we formulate counting as a matching
problem, enabling us to exploit the image self-similarity property that
naturally exists in object counting problems. We make the following three
contributions: first, a Generic Matching Network (GMN) architecture that can
potentially count any object in a class-agnostic manner; second, by
reformulating the counting problem as one of matching objects, we can take
advantage of the abundance of video data labeled for tracking, which contains
natural repetitions suitable for training a counting model. Such data enables
us to train the GMN. Third, to customize the GMN to different user
requirements, an adapter module is used to specialize the model with minimal
effort, i.e. using a few labeled examples, and adapting only a small fraction
of the trained parameters. This is a form of few-shot learning, which is
practical for domains where labels are limited due to requiring expert
knowledge (e.g. microbiology). We demonstrate the flexibility of our method on
a diverse set of existing counting benchmarks: specifically cells, cars, and
human crowds. The model achieves competitive performance on cell and crowd
counting datasets, and surpasses the state-of-the-art on the car dataset using
only three training images. When training on the entire dataset, the proposed
method outperforms all previous methods by a large margin.Comment: Asian Conference on Computer Vision (ACCV), 201
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