2,352 research outputs found
FCN-rLSTM: Deep Spatio-Temporal Neural Networks for Vehicle Counting in City Cameras
In this paper, we develop deep spatio-temporal neural networks to
sequentially count vehicles from low quality videos captured by city cameras
(citycams). Citycam videos have low resolution, low frame rate, high occlusion
and large perspective, making most existing methods lose their efficacy. To
overcome limitations of existing methods and incorporate the temporal
information of traffic video, we design a novel FCN-rLSTM network to jointly
estimate vehicle density and vehicle count by connecting fully convolutional
neural networks (FCN) with long short term memory networks (LSTM) in a residual
learning fashion. Such design leverages the strengths of FCN for pixel-level
prediction and the strengths of LSTM for learning complex temporal dynamics.
The residual learning connection reformulates the vehicle count regression as
learning residual functions with reference to the sum of densities in each
frame, which significantly accelerates the training of networks. To preserve
feature map resolution, we propose a Hyper-Atrous combination to integrate
atrous convolution in FCN and combine feature maps of different convolution
layers. FCN-rLSTM enables refined feature representation and a novel end-to-end
trainable mapping from pixels to vehicle count. We extensively evaluated the
proposed method on different counting tasks with three datasets, with
experimental results demonstrating their effectiveness and robustness. In
particular, FCN-rLSTM reduces the mean absolute error (MAE) from 5.31 to 4.21
on TRANCOS, and reduces the MAE from 2.74 to 1.53 on WebCamT. Training process
is accelerated by 5 times on average.Comment: Accepted by International Conference on Computer Vision (ICCV), 201
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
Ambient Sound Helps: Audiovisual Crowd Counting in Extreme Conditions
Visual crowd counting has been recently studied as a way to enable people
counting in crowd scenes from images. Albeit successful, vision-based crowd
counting approaches could fail to capture informative features in extreme
conditions, e.g., imaging at night and occlusion. In this work, we introduce a
novel task of audiovisual crowd counting, in which visual and auditory
information are integrated for counting purposes. We collect a large-scale
benchmark, named auDiovISual Crowd cOunting (DISCO) dataset, consisting of
1,935 images and the corresponding audio clips, and 170,270 annotated
instances. In order to fuse the two modalities, we make use of a linear
feature-wise fusion module that carries out an affine transformation on visual
and auditory features. Finally, we conduct extensive experiments using the
proposed dataset and approach. Experimental results show that introducing
auditory information can benefit crowd counting under different illumination,
noise, and occlusion conditions. The dataset and code will be released. Code
and data have been made availabl
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