7 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
"Seeing is Believing": Pedestrian Trajectory Forecasting Using Visual Frustum of Attention
In this paper we show the importance of the head pose estimation in the task of trajectory forecasting. This cue, when produced by an oracle and injected in a novel socially-based energy minimization approach, allows to get state-of-the-art performances on four different forecasting benchmarks, without relying on additional information such as expected destination and desired speed, which are supposed to be know beforehand for most of the current forecasting techniques. Our approach uses the head pose estimation for two aims: 1) to define a view frustum of attention, highlighting the people a given subject is more interested about, in order to avoid collisions; 2) to give a shorttime estimation of what would be the desired destination point. Moreover, we show that when the head pose estimation is given by a real detector, though the performance decreases, it still remains at the level of the top score forecasting systems
Measuring and understanding light in real life scenarios
Lighting design and modelling (the efficient and aesthetic placement of luminaires in a virtual or real scene) or industrial applications like luminaire planning and commissioning (the luminaire's installation and evaluation process along to the scene's geometry and structure) rely heavily on high realism and physically correct simulations. The current typical approaches are based only on CAD modeling simulations and offline rendering, with long processing times and therefore inflexible workflows. In this thesis we examine whether different camera-aided light modeling and numerical optimization approaches could be used to accurately understand, model and measure the light distribution in real life scenarios within real world environments. We show that factorization techniques could play a semantic role for light decomposition and light source identification, while we contribute a novel benchmark dataset and metrics for it. Thereafter we adapt a well known global illumination model (i.e. radiosity) and we extend it so that to overcome some of its basic limitations related to the assumption of point based only light sources or the adaption of only isotropic light perception sensors. We show that this extended radiosity numerical model can challenge the state-of-the-art in obtaining accurate dense spatial light measurements over time and in different scenarios. Finally we combine the latter model with human-centric sensing information and present how this could be beneficial for smart lighting applications related to quality lighting and power efficiency. Thus, with this work we contribute by setting the baselines for using an RGBD camera input as the only requirement to light modeling methods for light estimation in real life scenarios, and open a new applicability where the illumination modeling can be turned into an interactive process, allowing for real-time modifications and immediate feedback on the spatial illumination of a scene over time towards quality lighting and energy efficient solutions