220 research outputs found
CDC: Convolutional-De-Convolutional Networks for Precise Temporal Action Localization in Untrimmed Videos
Temporal action localization is an important yet challenging problem. Given a
long, untrimmed video consisting of multiple action instances and complex
background contents, we need not only to recognize their action categories, but
also to localize the start time and end time of each instance. Many
state-of-the-art systems use segment-level classifiers to select and rank
proposal segments of pre-determined boundaries. However, a desirable model
should move beyond segment-level and make dense predictions at a fine
granularity in time to determine precise temporal boundaries. To this end, we
design a novel Convolutional-De-Convolutional (CDC) network that places CDC
filters on top of 3D ConvNets, which have been shown to be effective for
abstracting action semantics but reduce the temporal length of the input data.
The proposed CDC filter performs the required temporal upsampling and spatial
downsampling operations simultaneously to predict actions at the frame-level
granularity. It is unique in jointly modeling action semantics in space-time
and fine-grained temporal dynamics. We train the CDC network in an end-to-end
manner efficiently. Our model not only achieves superior performance in
detecting actions in every frame, but also significantly boosts the precision
of localizing temporal boundaries. Finally, the CDC network demonstrates a very
high efficiency with the ability to process 500 frames per second on a single
GPU server. We will update the camera-ready version and publish the source
codes online soon.Comment: IEEE Conference on Computer Vision and Pattern Recognition (CVPR),
201
Single Shot Temporal Action Detection
Temporal action detection is a very important yet challenging problem, since
videos in real applications are usually long, untrimmed and contain multiple
action instances. This problem requires not only recognizing action categories
but also detecting start time and end time of each action instance. Many
state-of-the-art methods adopt the "detection by classification" framework:
first do proposal, and then classify proposals. The main drawback of this
framework is that the boundaries of action instance proposals have been fixed
during the classification step. To address this issue, we propose a novel
Single Shot Action Detector (SSAD) network based on 1D temporal convolutional
layers to skip the proposal generation step via directly detecting action
instances in untrimmed video. On pursuit of designing a particular SSAD network
that can work effectively for temporal action detection, we empirically search
for the best network architecture of SSAD due to lacking existing models that
can be directly adopted. Moreover, we investigate into input feature types and
fusion strategies to further improve detection accuracy. We conduct extensive
experiments on two challenging datasets: THUMOS 2014 and MEXaction2. When
setting Intersection-over-Union threshold to 0.5 during evaluation, SSAD
significantly outperforms other state-of-the-art systems by increasing mAP from
19.0% to 24.6% on THUMOS 2014 and from 7.4% to 11.0% on MEXaction2.Comment: ACM Multimedia 201
Every Moment Counts: Dense Detailed Labeling of Actions in Complex Videos
Every moment counts in action recognition. A comprehensive understanding of
human activity in video requires labeling every frame according to the actions
occurring, placing multiple labels densely over a video sequence. To study this
problem we extend the existing THUMOS dataset and introduce MultiTHUMOS, a new
dataset of dense labels over unconstrained internet videos. Modeling multiple,
dense labels benefits from temporal relations within and across classes. We
define a novel variant of long short-term memory (LSTM) deep networks for
modeling these temporal relations via multiple input and output connections. We
show that this model improves action labeling accuracy and further enables
deeper understanding tasks ranging from structured retrieval to action
prediction.Comment: To appear in IJC
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