11,869 research outputs found
Transformer Networks for Trajectory Forecasting
Most recent successes on forecasting the people motion are based on LSTM
models and all most recent progress has been achieved by modelling the social
interaction among people and the people interaction with the scene. We question
the use of the LSTM models and propose the novel use of Transformer Networks
for trajectory forecasting. This is a fundamental switch from the sequential
step-by-step processing of LSTMs to the only-attention-based memory mechanisms
of Transformers. In particular, we consider both the original Transformer
Network (TF) and the larger Bidirectional Transformer (BERT), state-of-the-art
on all natural language processing tasks. Our proposed Transformers predict the
trajectories of the individual people in the scene. These are "simple" model
because each person is modelled separately without any complex human-human nor
scene interaction terms. In particular, the TF model without bells and whistles
yields the best score on the largest and most challenging trajectory
forecasting benchmark of TrajNet. Additionally, its extension which predicts
multiple plausible future trajectories performs on par with more engineered
techniques on the 5 datasets of ETH + UCY. Finally, we show that Transformers
may deal with missing observations, as it may be the case with real sensor
data. Code is available at https://github.com/FGiuliari/Trajectory-Transformer.Comment: 18 pages, 3 figure
Activity Recognition and Prediction in Real Homes
In this paper, we present work in progress on activity recognition and
prediction in real homes using either binary sensor data or depth video data.
We present our field trial and set-up for collecting and storing the data, our
methods, and our current results. We compare the accuracy of predicting the
next binary sensor event using probabilistic methods and Long Short-Term Memory
(LSTM) networks, include the time information to improve prediction accuracy,
as well as predict both the next sensor event and its mean time of occurrence
using one LSTM model. We investigate transfer learning between apartments and
show that it is possible to pre-train the model with data from other apartments
and achieve good accuracy in a new apartment straight away. In addition, we
present preliminary results from activity recognition using low-resolution
depth video data from seven apartments, and classify four activities - no
movement, standing up, sitting down, and TV interaction - by using a relatively
simple processing method where we apply an Infinite Impulse Response (IIR)
filter to extract movements from the frames prior to feeding them to a
convolutional LSTM network for the classification.Comment: 12 pages, Symposium of the Norwegian AI Society NAIS 201
Forecasting People Trajectories and Head Poses by Jointly Reasoning on Tracklets and Vislets
In this work, we explore the correlation between people trajectories and
their head orientations. We argue that people trajectory and head pose
forecasting can be modelled as a joint problem. Recent approaches on trajectory
forecasting leverage short-term trajectories (aka tracklets) of pedestrians to
predict their future paths. In addition, sociological cues, such as expected
destination or pedestrian interaction, are often combined with tracklets. In
this paper, we propose MiXing-LSTM (MX-LSTM) to capture the interplay between
positions and head orientations (vislets) thanks to a joint unconstrained
optimization of full covariance matrices during the LSTM backpropagation. We
additionally exploit the head orientations as a proxy for the visual attention,
when modeling social interactions. MX-LSTM predicts future pedestrians location
and head pose, increasing the standard capabilities of the current approaches
on long-term trajectory forecasting. Compared to the state-of-the-art, our
approach shows better performances on an extensive set of public benchmarks.
MX-LSTM is particularly effective when people move slowly, i.e. the most
challenging scenario for all other models. The proposed approach also allows
for accurate predictions on a longer time horizon.Comment: Accepted at IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE
INTELLIGENCE 2019. arXiv admin note: text overlap with arXiv:1805.0065
Predicting Deeper into the Future of Semantic Segmentation
The ability to predict and therefore to anticipate the future is an important
attribute of intelligence. It is also of utmost importance in real-time
systems, e.g. in robotics or autonomous driving, which depend on visual scene
understanding for decision making. While prediction of the raw RGB pixel values
in future video frames has been studied in previous work, here we introduce the
novel task of predicting semantic segmentations of future frames. Given a
sequence of video frames, our goal is to predict segmentation maps of not yet
observed video frames that lie up to a second or further in the future. We
develop an autoregressive convolutional neural network that learns to
iteratively generate multiple frames. Our results on the Cityscapes dataset
show that directly predicting future segmentations is substantially better than
predicting and then segmenting future RGB frames. Prediction results up to half
a second in the future are visually convincing and are much more accurate than
those of a baseline based on warping semantic segmentations using optical flow.Comment: Accepted to ICCV 2017. Supplementary material available on the
authors' webpage
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