52,723 research outputs found
Seeing What You're Told: Sentence-Guided Activity Recognition In Video
We present a system that demonstrates how the compositional structure of
events, in concert with the compositional structure of language, can interplay
with the underlying focusing mechanisms in video action recognition, thereby
providing a medium, not only for top-down and bottom-up integration, but also
for multi-modal integration between vision and language. We show how the roles
played by participants (nouns), their characteristics (adjectives), the actions
performed (verbs), the manner of such actions (adverbs), and changing spatial
relations between participants (prepositions) in the form of whole sentential
descriptions mediated by a grammar, guides the activity-recognition process.
Further, the utility and expressiveness of our framework is demonstrated by
performing three separate tasks in the domain of multi-activity videos:
sentence-guided focus of attention, generation of sentential descriptions of
video, and query-based video search, simply by leveraging the framework in
different manners.Comment: To appear in CVPR 201
To dash or to dawdle: verb-associated speed of motion influences eye movements during spoken sentence comprehension
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
Motion analysis report
Human motion analysis is the task of converting actual human movements into computer readable data. Such movement information may be obtained though active or passive sensing methods. Active methods include physical measuring devices such as goniometers on joints of the body, force plates, and manually operated sensors such as a Cybex dynamometer. Passive sensing de-couples the position measuring device from actual human contact. Passive sensors include Selspot scanning systems (since there is no mechanical connection between the subject's attached LEDs and the infrared sensing cameras), sonic (spark-based) three-dimensional digitizers, Polhemus six-dimensional tracking systems, and image processing systems based on multiple views and photogrammetric calculations
Hybrid Scene Compression for Visual Localization
Localizing an image wrt. a 3D scene model represents a core task for many
computer vision applications. An increasing number of real-world applications
of visual localization on mobile devices, e.g., Augmented Reality or autonomous
robots such as drones or self-driving cars, demand localization approaches to
minimize storage and bandwidth requirements. Compressing the 3D models used for
localization thus becomes a practical necessity. In this work, we introduce a
new hybrid compression algorithm that uses a given memory limit in a more
effective way. Rather than treating all 3D points equally, it represents a
small set of points with full appearance information and an additional, larger
set of points with compressed information. This enables our approach to obtain
a more complete scene representation without increasing the memory
requirements, leading to a superior performance compared to previous
compression schemes. As part of our contribution, we show how to handle
ambiguous matches arising from point compression during RANSAC. Besides
outperforming previous compression techniques in terms of pose accuracy under
the same memory constraints, our compression scheme itself is also more
efficient. Furthermore, the localization rates and accuracy obtained with our
approach are comparable to state-of-the-art feature-based methods, while using
a small fraction of the memory.Comment: Published at CVPR 201
Grounding the Lexical Semantics of Verbs in Visual Perception using Force Dynamics and Event Logic
This paper presents an implemented system for recognizing the occurrence of
events described by simple spatial-motion verbs in short image sequences. The
semantics of these verbs is specified with event-logic expressions that
describe changes in the state of force-dynamic relations between the
participants of the event. An efficient finite representation is introduced for
the infinite sets of intervals that occur when describing liquid and
semi-liquid events. Additionally, an efficient procedure using this
representation is presented for inferring occurrences of compound events,
described with event-logic expressions, from occurrences of primitive events.
Using force dynamics and event logic to specify the lexical semantics of events
allows the system to be more robust than prior systems based on motion profile
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