33,476 research outputs found

    To dash or to dawdle: verb-associated speed of motion influences eye movements during spoken sentence comprehension

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    In describing motion events verbs of manner provide information about the speed of agents or objects in those events. We used eye tracking to investigate how inferences about this verb-associated speed of motion would influence the time course of attention to a visual scene that matched an event described in language. Eye movements were recorded as participants heard spoken sentences with verbs that implied a fast (“dash”) or slow (“dawdle”) movement of an agent towards a goal. These sentences were heard whilst participants concurrently looked at scenes depicting the agent and a path which led to the goal object. Our results indicate a mapping of events onto the visual scene consistent with participants mentally simulating the movement of the agent along the path towards the goal: when the verb implies a slow manner of motion, participants look more often and longer along the path to the goal; when the verb implies a fast manner of motion, participants tend to look earlier at the goal and less on the path. These results reveal that event comprehension in the presence of a visual world involves establishing and dynamically updating the locations of entities in response to linguistic descriptions of events

    Realtime Multilevel Crowd Tracking using Reciprocal Velocity Obstacles

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    We present a novel, realtime algorithm to compute the trajectory of each pedestrian in moderately dense crowd scenes. Our formulation is based on an adaptive particle filtering scheme that uses a multi-agent motion model based on velocity-obstacles, and takes into account local interactions as well as physical and personal constraints of each pedestrian. Our method dynamically changes the number of particles allocated to each pedestrian based on different confidence metrics. Additionally, we use a new high-definition crowd video dataset, which is used to evaluate the performance of different pedestrian tracking algorithms. This dataset consists of videos of indoor and outdoor scenes, recorded at different locations with 30-80 pedestrians. We highlight the performance benefits of our algorithm over prior techniques using this dataset. In practice, our algorithm can compute trajectories of tens of pedestrians on a multi-core desktop CPU at interactive rates (27-30 frames per second). To the best of our knowledge, our approach is 4-5 times faster than prior methods, which provide similar accuracy

    Adaptive planning for distributed systems using goal accomplishment tracking

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    Goal accomplishment tracking is the process of monitoring the progress of a task or series of tasks towards completing a goal. Goal accomplishment tracking is used to monitor goal progress in a variety of domains, including workflow processing, teleoperation and industrial manufacturing. Practically, it involves the constant monitoring of task execution, analysis of this data to determine the task progress and notification of interested parties. This information is usually used in a passive way to observe goal progress. However, responding to this information may prevent goal failures. In addition, responding proactively in an opportunistic way can also lead to goals being completed faster. This paper proposes an architecture to support the adaptive planning of tasks for fault tolerance or opportunistic task execution based on goal accomplishment tracking. It argues that dramatically increased performance can be gained by monitoring task execution and altering plans dynamically
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