Papers by Jiaming Liang (CH)
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020
In group object tracking, the identification of the group leader can be highly beneficial for pre... more In group object tracking, the identification of the group leader can be highly beneficial for predicting the intention and future manoeuvres of objects as well as learning the underlying group behaviour traits. This paper presents an online approach for inferring dominant entities in tracked groups from observations. Unlike traditional leader-follower models, here we develop a new rotated leadership model that can capture the dynamic evolution of the interaction patterns in groups over time. Two methods, an online Gibbs sampler and deterministic particle filter, are then designed to infer sequentially the leader in group object tracking scenarios. Synthetic and real pigeon flocking data are used to demonstrate the effectiveness of the proposed techniques in terms of identifying the group leader under complex dynamics.
2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP), 2019
The motion of a tracked object often has long term underlying dependencies due to premeditated ac... more The motion of a tracked object often has long term underlying dependencies due to premeditated actions dictated by intent, such as destination. Revealing this intent, as early as possible, can enable advanced intelligent system functionalities for conflict/opportunity detection and automated decision making, for instance in surveillance and human computer interaction. This paper presents a novel Bayesian intent inference framework that utilises sequential Monte Carlo (SMC) methods to determine the destination of a tracked object exhibiting unknown jump behaviour. The latter can arise from the object undertaking fast maneuvers (e.g. for obstacle avoidance) and/or due to external uncontrollable environmental perturbations. Suitable intent-driven stochastic models and inference routines are introduced. The effectiveness of the proposed approach is demonstrated using synthetic and real data.
2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP), 2020
The motion of an object (e.g. ship, jet, pedestrian, bird, drone, etc.) is usually governed by pr... more The motion of an object (e.g. ship, jet, pedestrian, bird, drone, etc.) is usually governed by premeditated actions as per an underlying intent, for instance reaching a destination. In this paper, we introduce a novel intent-driven dynamical model based on a continuous-time intrinsic coordinate model. By combining this model with particle filtering, a seamless approach for jointly predicting the destination and estimating the state of a highly manoeuvrable object is developed. We examine the proposed inference technique using real data with different measurement models to demonstrate its efficacy. In particular, we show that the introduced approach can be a flexible and competitive alternative, in terms of prediction and estimation performance, to other existing methods for various measurement models including nonlinear ones.
Data-Centric Engineering, 2020
In various scenarios, the motion of a tracked object, for example, a pointing apparatus, pedestri... more In various scenarios, the motion of a tracked object, for example, a pointing apparatus, pedestrian, animal, vehicle, and others, is driven by achieving a premeditated goal such as reaching a destination. This is albeit the various possible trajectories to this endpoint. This paper presents a generic Bayesian framework that utilizes stochastic models that can capture the influence of intent (viz., destination) on the object behavior. It leads to simple algorithms to infer, as early as possible, the intended endpoint from noisy sensory observations, with relatively low computational and training data requirements. This framework is introduced in the context of the novel predictive touch technology for intelligent user interfaces and touchless interactions. It can determine, early in the interaction task or pointing gesture, the interface item the user intends to select on the display (e.g., touchscreen) and accordingly simplify as well as expedite the selection task. This is shown to...
IEEE Transactions on Intelligent Transportation Systems, 2019
The objective of this paper is twofold. First, it presents a brief overview of existing driver an... more The objective of this paper is twofold. First, it presents a brief overview of existing driver and passenger identification or recognition approaches which rely on smartphone data. This includes listing the typically available sensory measurements and highlighting a few key practical considerations for automotive settings. Second, a simple identification method that utilises the smartphone inertial measurements and, possibly, doors signal is proposed. It is based on analysing the user behaviour during entry, namely the direction of turning, and extracting relevant salient features, which are distinctive depending on the side of entry to the vehicle. This is followed by applying a suitable classifier and decision criterion. Experimental data is shown to demonstrate the usefulness and effectiveness of the introduced probabilistic, low-complexity, identification technique.
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Papers by Jiaming Liang (CH)