11,963 research outputs found
Detecting events and key actors in multi-person videos
Multi-person event recognition is a challenging task, often with many people
active in the scene but only a small subset contributing to an actual event. In
this paper, we propose a model which learns to detect events in such videos
while automatically "attending" to the people responsible for the event. Our
model does not use explicit annotations regarding who or where those people are
during training and testing. In particular, we track people in videos and use a
recurrent neural network (RNN) to represent the track features. We learn
time-varying attention weights to combine these features at each time-instant.
The attended features are then processed using another RNN for event
detection/classification. Since most video datasets with multiple people are
restricted to a small number of videos, we also collected a new basketball
dataset comprising 257 basketball games with 14K event annotations
corresponding to 11 event classes. Our model outperforms state-of-the-art
methods for both event classification and detection on this new dataset.
Additionally, we show that the attention mechanism is able to consistently
localize the relevant players.Comment: Accepted for publication in CVPR'1
PersonRank: Detecting Important People in Images
Always, some individuals in images are more important/attractive than others
in some events such as presentation, basketball game or speech. However, it is
challenging to find important people among all individuals in images directly
based on their spatial or appearance information due to the existence of
diverse variations of pose, action, appearance of persons and various changes
of occasions. We overcome this difficulty by constructing a multiple
Hyper-Interaction Graph to treat each individual in an image as a node and
inferring the most active node referring to interactions estimated by various
types of clews. We model pairwise interactions between persons as the edge
message communicated between nodes, resulting in a bidirectional
pairwise-interaction graph. To enrich the personperson interaction estimation,
we further introduce a unidirectional hyper-interaction graph that models the
consensus of interaction between a focal person and any person in a local
region around. Finally, we modify the PageRank algorithm to infer the
activeness of persons on the multiple Hybrid-Interaction Graph (HIG), the union
of the pairwise-interaction and hyperinteraction graphs, and we call our
algorithm the PersonRank. In order to provide publicable datasets for
evaluation, we have contributed a new dataset called Multi-scene Important
People Image Dataset and gathered a NCAA Basketball Image Dataset from sports
game sequences. We have demonstrated that the proposed PersonRank outperforms
related methods clearly and substantially.Comment: 8 pages, conferenc
Towards Structured Analysis of Broadcast Badminton Videos
Sports video data is recorded for nearly every major tournament but remains
archived and inaccessible to large scale data mining and analytics. It can only
be viewed sequentially or manually tagged with higher-level labels which is
time consuming and prone to errors. In this work, we propose an end-to-end
framework for automatic attributes tagging and analysis of sport videos. We use
commonly available broadcast videos of matches and, unlike previous approaches,
does not rely on special camera setups or additional sensors.
Our focus is on Badminton as the sport of interest. We propose a method to
analyze a large corpus of badminton broadcast videos by segmenting the points
played, tracking and recognizing the players in each point and annotating their
respective badminton strokes. We evaluate the performance on 10 Olympic matches
with 20 players and achieved 95.44% point segmentation accuracy, 97.38% player
detection score ([email protected]), 97.98% player identification accuracy, and stroke
segmentation edit scores of 80.48%. We further show that the automatically
annotated videos alone could enable the gameplay analysis and inference by
computing understandable metrics such as player's reaction time, speed, and
footwork around the court, etc.Comment: 9 page
Early Recognition of Human Activities from First-Person Videos Using Onset Representations
In this paper, we propose a methodology for early recognition of human
activities from videos taken with a first-person viewpoint. Early recognition,
which is also known as activity prediction, is an ability to infer an ongoing
activity at its early stage. We present an algorithm to perform recognition of
activities targeted at the camera from streaming videos, making the system to
predict intended activities of the interacting person and avoid harmful events
before they actually happen. We introduce the novel concept of 'onset' that
efficiently summarizes pre-activity observations, and design an approach to
consider event history in addition to ongoing video observation for early
first-person recognition of activities. We propose to represent onset using
cascade histograms of time series gradients, and we describe a novel
algorithmic setup to take advantage of onset for early recognition of
activities. The experimental results clearly illustrate that the proposed
concept of onset enables better/earlier recognition of human activities from
first-person videos
AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions
This paper introduces a video dataset of spatio-temporally localized Atomic
Visual Actions (AVA). The AVA dataset densely annotates 80 atomic visual
actions in 430 15-minute video clips, where actions are localized in space and
time, resulting in 1.58M action labels with multiple labels per person
occurring frequently. The key characteristics of our dataset are: (1) the
definition of atomic visual actions, rather than composite actions; (2) precise
spatio-temporal annotations with possibly multiple annotations for each person;
(3) exhaustive annotation of these atomic actions over 15-minute video clips;
(4) people temporally linked across consecutive segments; and (5) using movies
to gather a varied set of action representations. This departs from existing
datasets for spatio-temporal action recognition, which typically provide sparse
annotations for composite actions in short video clips. We will release the
dataset publicly.
AVA, with its realistic scene and action complexity, exposes the intrinsic
difficulty of action recognition. To benchmark this, we present a novel
approach for action localization that builds upon the current state-of-the-art
methods, and demonstrates better performance on JHMDB and UCF101-24 categories.
While setting a new state of the art on existing datasets, the overall results
on AVA are low at 15.6% mAP, underscoring the need for developing new
approaches for video understanding.Comment: To appear in CVPR 2018. Check dataset page
https://research.google.com/ava/ for detail
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