29,502 research outputs found
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
DeepSignals: Predicting Intent of Drivers Through Visual Signals
Detecting the intention of drivers is an essential task in self-driving,
necessary to anticipate sudden events like lane changes and stops. Turn signals
and emergency flashers communicate such intentions, providing seconds of
potentially critical reaction time. In this paper, we propose to detect these
signals in video sequences by using a deep neural network that reasons about
both spatial and temporal information. Our experiments on more than a million
frames show high per-frame accuracy in very challenging scenarios.Comment: To be presented at the IEEE International Conference on Robotics and
Automation (ICRA), 201
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
Convolutional Neural Network on Three Orthogonal Planes for Dynamic Texture Classification
Dynamic Textures (DTs) are sequences of images of moving scenes that exhibit
certain stationarity properties in time such as smoke, vegetation and fire. The
analysis of DT is important for recognition, segmentation, synthesis or
retrieval for a range of applications including surveillance, medical imaging
and remote sensing. Deep learning methods have shown impressive results and are
now the new state of the art for a wide range of computer vision tasks
including image and video recognition and segmentation. In particular,
Convolutional Neural Networks (CNNs) have recently proven to be well suited for
texture analysis with a design similar to a filter bank approach. In this
paper, we develop a new approach to DT analysis based on a CNN method applied
on three orthogonal planes x y , xt and y t . We train CNNs on spatial frames
and temporal slices extracted from the DT sequences and combine their outputs
to obtain a competitive DT classifier. Our results on a wide range of commonly
used DT classification benchmark datasets prove the robustness of our approach.
Significant improvement of the state of the art is shown on the larger
datasets.Comment: 19 pages, 10 figure
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