1,251 research outputs found
What Will I Do Next? The Intention from Motion Experiment
In computer vision, video-based approaches have been widely explored for the
early classification and the prediction of actions or activities. However, it
remains unclear whether this modality (as compared to 3D kinematics) can still
be reliable for the prediction of human intentions, defined as the overarching
goal embedded in an action sequence. Since the same action can be performed
with different intentions, this problem is more challenging but yet affordable
as proved by quantitative cognitive studies which exploit the 3D kinematics
acquired through motion capture systems. In this paper, we bridge cognitive and
computer vision studies, by demonstrating the effectiveness of video-based
approaches for the prediction of human intentions. Precisely, we propose
Intention from Motion, a new paradigm where, without using any contextual
information, we consider instantaneous grasping motor acts involving a bottle
in order to forecast why the bottle itself has been reached (to pass it or to
place in a box, or to pour or to drink the liquid inside). We process only the
grasping onsets casting intention prediction as a classification framework.
Leveraging on our multimodal acquisition (3D motion capture data and 2D optical
videos), we compare the most commonly used 3D descriptors from cognitive
studies with state-of-the-art video-based techniques. Since the two analyses
achieve an equivalent performance, we demonstrate that computer vision tools
are effective in capturing the kinematics and facing the cognitive problem of
human intention prediction.Comment: 2017 IEEE Conference on Computer Vision and Pattern Recognition
Workshop
Jump Particle Filtering Framework for Joint Target Tracking and Intent Recognition
This paper presents a Bayesian framework for inferring the posterior of the
extended state of a target, incorporating its underlying goal or intent, such
as any intermediate waypoints and/or final destination. The methodology is thus
for joint tracking and intent recognition. Several novel latent intent models
are proposed here within a virtual leader formulation. They capture the
influence of the target's hidden goal on its instantaneous behaviour. In this
context, various motion models, including for highly maneuvering objects, are
also considered. The a priori unknown target intent (e.g. destination) can
dynamically change over time and take any value within the state space (e.g. a
location or spatial region). A sequential Monte Carlo (particle filtering)
approach is introduced for the simultaneous estimation of the target's
(kinematic) state and its intent. Rao-Blackwellisation is employed to enhance
the statistical performance of the inference routine. Simulated data and real
radar measurements are used to demonstrate the efficacy of the proposed
techniques.Comment: Submitted to IEEE Transactions on Aerospace and Electronic Systems
(T-AES
iPLAN: Intent-Aware Planning in Heterogeneous Traffic via Distributed Multi-Agent Reinforcement Learning
Navigating safely and efficiently in dense and heterogeneous traffic
scenarios is challenging for autonomous vehicles (AVs) due to their inability
to infer the behaviors or intentions of nearby drivers. In this work, we
introduce a distributed multi-agent reinforcement learning (MARL) algorithm
that can predict trajectories and intents in dense and heterogeneous traffic
scenarios. Our approach for intent-aware planning, iPLAN, allows agents to
infer nearby drivers' intents solely from their local observations. We model
two distinct incentives for agents' strategies: Behavioral Incentive for
high-level decision-making based on their driving behavior or personality and
Instant Incentive for motion planning for collision avoidance based on the
current traffic state. Our approach enables agents to infer their opponents'
behavior incentives and integrate this inferred information into their
decision-making and motion-planning processes. We perform experiments on two
simulation environments, Non-Cooperative Navigation and Heterogeneous Highway.
In Heterogeneous Highway, results show that, compared with centralized training
decentralized execution (CTDE) MARL baselines such as QMIX and MAPPO, our
method yields a 4.3% and 38.4% higher episodic reward in mild and chaotic
traffic, with 48.1% higher success rate and 80.6% longer survival time in
chaotic traffic. We also compare with a decentralized training decentralized
execution (DTDE) baseline IPPO and demonstrate a higher episodic reward of
12.7% and 6.3% in mild traffic and chaotic traffic, 25.3% higher success rate,
and 13.7% longer survival time
Detection of malicious intent in non-cooperative drone surveillance
In this paper, a Bayesian approach is proposed for the early detection of a drone threatening or anomalous behaviour in a surveyed region. This is in relation to revealing, as early as possible, the drone intent to either leave a geographical area where it is authorised to fly (e.g. to conduct inspection work) or reach a prohibited zone (e.g. runway protection zones at airports or a critical infrastructure site). The inference here is based on the noisy sensory observations of the target state from a non-cooperative surveillance system such as a radar. Data from Aveillant's Gamekeeper radar from a live drone trial is used to illustrate the efficacy of the introduced approach
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Bayesian Approaches to Tracking, Sensor Fusion and Intent Prediction
This thesis presents work on the development of model-based Bayesian approaches to object tracking and intent prediction. Successful navigation/positioning applications rely fundamentally on the choice of appropriate dynamic model and the design of effective tracking algorithms capable of maximising the use of the structure of the dynamic system and the information from sensors. While the tracking problem with frequent and accurate position data has been well studied, we push back the frontiers of current technology where an object can undergo fast manoeuvres and position fixes are limited. On the other hand, intent prediction techniques which extract higher level information such as the intended destination of a moving object can be designed, given the ability to perform successful tracking. Such techniques can play important roles in various application areas, including traffic monitoring, intelligent human computer interaction systems and autonomous route planning.
In the first part of this thesis Bayesian tracking methods are designed based on a standard fix-rate setting in which the dynamic system is formulated into a Markovian state space form. We show that the combination of an intrinsic coordinate dynamic model and sensors in the object's body frame leads to novel state space models according to which efficient proposal kernels can be designed and implemented by the sequential Monte Carlo (SMC) methods. Also, sequential Markov chain Monte Carlo schemes are considered for the first time to tackle the sequential batch inference problems due to the presence of infrequent position data. Performance evaluation on both synthetic and real-world data shows that the proposed algorithms are superior to simpler particle filters, implying that they can be favourable alternatives to tracking problems with inertial sensors.
The modelling assumption that leads to Markovian state space models can be restrictive for real-world systems as it stipulates that the state sequence has to be synchronised with the observations. In the second major part of this thesis we relax this assumption and work with a more natural class of models, termed variable rate models. We generalise the existing variable rate intrinsic model to incorporate acceleration, speed, distance and position data and introduce new variable rate particle filtering methods tailored to the derived model to accommodate multi-sensor multi-rate tracking scenarios. The proposed algorithms can achieve substantial improvements in terms of tracking accuracy and robustness over a bootstrap variable rate particle filter. Moreover, full Bayesian inference schemes for the learning of both the hidden state and system parameters are presented, with numerical results illustrating their effectiveness.
The last part of the thesis is about designing efficient intent prediction algorithms within a Bayesian framework. A pseudo-observation based approach to the incorporation of destination knowledge is introduced, making the mathematics of the dynamical model and the observation process consistent with the Markov state process. Based on the new interpretation, two algorithms are proposed to sequentially estimate the probability of all possible endpoints. Whilst the synthetic maritime surveillance data demonstrate that the proposed methods can achieve comparable prediction performance with reduced computational cost in comparison to the existing bridging distribution based methods, the results on an extensive freehand pointing database, which contains 95 three-dimensional pointing trajectories, show that the new algorithms can outperform other state-of-the-art techniques. Some sensitivity tests are also performed, confirming the good robustness of the introduced methods against model mismatches
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