2,546 research outputs found
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
MX-LSTM: mixing tracklets and vislets to jointly forecast trajectories and head poses
Recent approaches on trajectory forecasting use tracklets to predict the
future positions of pedestrians exploiting Long Short Term Memory (LSTM)
architectures. This paper shows that adding vislets, that is, short sequences
of head pose estimations, allows to increase significantly the trajectory
forecasting performance. We then propose to use vislets in a novel framework
called MX-LSTM, capturing the interplay between tracklets and vislets thanks to
a joint unconstrained optimization of full covariance matrices during the LSTM
backpropagation. At the same time, MX-LSTM predicts the future head poses,
increasing the standard capabilities of the long-term trajectory forecasting
approaches. With standard head pose estimators and an attentional-based social
pooling, MX-LSTM scores the new trajectory forecasting state-of-the-art in all
the considered datasets (Zara01, Zara02, UCY, and TownCentre) with a dramatic
margin when the pedestrians slow down, a case where most of the forecasting
approaches struggle to provide an accurate solution.Comment: 10 pages, 3 figures to appear in CVPR 201
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
Social and Scene-Aware Trajectory Prediction in Crowded Spaces
Mimicking human ability to forecast future positions or interpret complex
interactions in urban scenarios, such as streets, shopping malls or squares, is
essential to develop socially compliant robots or self-driving cars. Autonomous
systems may gain advantage on anticipating human motion to avoid collisions or
to naturally behave alongside people. To foresee plausible trajectories, we
construct an LSTM (long short-term memory)-based model considering three
fundamental factors: people interactions, past observations in terms of
previously crossed areas and semantics of surrounding space. Our model
encompasses several pooling mechanisms to join the above elements defining
multiple tensors, namely social, navigation and semantic tensors. The network
is tested in unstructured environments where complex paths emerge according to
both internal (intentions) and external (other people, not accessible areas)
motivations. As demonstrated, modeling paths unaware of social interactions or
context information, is insufficient to correctly predict future positions.
Experimental results corroborate the effectiveness of the proposed framework in
comparison to LSTM-based models for human path prediction.Comment: Accepted to ICCV 2019 Workshop on Assistive Computer Vision and
Robotics (ACVR
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