24,887 research outputs found
Statistical Analysis of Dynamic Actions
Real-world action recognition applications require the development of systems which are fast, can handle a large variety of actions without a priori knowledge of the type of actions, need a minimal number of parameters, and necessitate as short as possible learning stage. In this paper, we suggest such an approach. We regard dynamic activities as long-term temporal objects, which are characterized by spatio-temporal features at multiple temporal scales. Based on this, we design a simple statistical distance measure between video sequences which captures the similarities in their behavioral content. This measure is nonparametric and can thus handle a wide range of complex dynamic actions. Having a behavior-based distance measure between sequences, we use it for a variety of tasks, including: video indexing, temporal segmentation, and action-based video clustering. These tasks are performed without prior knowledge of the types of actions, their models, or their temporal extents
Fast, invariant representation for human action in the visual system
Humans can effortlessly recognize others' actions in the presence of complex
transformations, such as changes in viewpoint. Several studies have located the
regions in the brain involved in invariant action recognition, however, the
underlying neural computations remain poorly understood. We use
magnetoencephalography (MEG) decoding and a dataset of well-controlled,
naturalistic videos of five actions (run, walk, jump, eat, drink) performed by
different actors at different viewpoints to study the computational steps used
to recognize actions across complex transformations. In particular, we ask when
the brain discounts changes in 3D viewpoint relative to when it initially
discriminates between actions. We measure the latency difference between
invariant and non-invariant action decoding when subjects view full videos as
well as form-depleted and motion-depleted stimuli. Our results show no
difference in decoding latency or temporal profile between invariant and
non-invariant action recognition in full videos. However, when either form or
motion information is removed from the stimulus set, we observe a decrease and
delay in invariant action decoding. Our results suggest that the brain
recognizes actions and builds invariance to complex transformations at the same
time, and that both form and motion information are crucial for fast, invariant
action recognition
Content-based Video Retrieval by Integrating Spatio-Temporal and Stochastic Recognition of Events
As amounts of publicly available video data grow the need to query this data efficiently becomes significant. Consequently content-based retrieval of video data turns out to be a challenging and important problem. We address the specific aspect of inferring semantics automatically from raw video data. In particular, we introduce a new video data model that supports the integrated use of two different approaches for mapping low-level features to high-level concepts. Firstly, the model is extended with a rule-based approach that supports spatio-temporal formalization of high-level concepts, and then with a stochastic approach. Furthermore, results on real tennis video data are presented, demonstrating the validity of both approaches, as well us advantages of their integrated us
Invariance of visual operations at the level of receptive fields
Receptive field profiles registered by cell recordings have shown that
mammalian vision has developed receptive fields tuned to different sizes and
orientations in the image domain as well as to different image velocities in
space-time. This article presents a theoretical model by which families of
idealized receptive field profiles can be derived mathematically from a small
set of basic assumptions that correspond to structural properties of the
environment. The article also presents a theory for how basic invariance
properties to variations in scale, viewing direction and relative motion can be
obtained from the output of such receptive fields, using complementary
selection mechanisms that operate over the output of families of receptive
fields tuned to different parameters. Thereby, the theory shows how basic
invariance properties of a visual system can be obtained already at the level
of receptive fields, and we can explain the different shapes of receptive field
profiles found in biological vision from a requirement that the visual system
should be invariant to the natural types of image transformations that occur in
its environment.Comment: 40 pages, 17 figure
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