122 research outputs found
Deep Learning for Detecting Multiple Space-Time Action Tubes in Videos
In this work, we propose an approach to the spatiotemporal localisation
(detection) and classification of multiple concurrent actions within temporally
untrimmed videos. Our framework is composed of three stages. In stage 1,
appearance and motion detection networks are employed to localise and score
actions from colour images and optical flow. In stage 2, the appearance network
detections are boosted by combining them with the motion detection scores, in
proportion to their respective spatial overlap. In stage 3, sequences of
detection boxes most likely to be associated with a single action instance,
called action tubes, are constructed by solving two energy maximisation
problems via dynamic programming. While in the first pass, action paths
spanning the whole video are built by linking detection boxes over time using
their class-specific scores and their spatial overlap, in the second pass,
temporal trimming is performed by ensuring label consistency for all
constituting detection boxes. We demonstrate the performance of our algorithm
on the challenging UCF101, J-HMDB-21 and LIRIS-HARL datasets, achieving new
state-of-the-art results across the board and significantly increasing
detection speed at test time. We achieve a huge leap forward in action
detection performance and report a 20% and 11% gain in mAP (mean average
precision) on UCF-101 and J-HMDB-21 datasets respectively when compared to the
state-of-the-art.Comment: Accepted by British Machine Vision Conference 201
Spotting Agreement and Disagreement: A Survey of Nonverbal Audiovisual Cues and Tools
While detecting and interpreting temporal patterns of non–verbal behavioral cues in a given context is a natural and often unconscious process for humans, it remains a rather difficult task for computer systems. Nevertheless, it is an important one to achieve if the goal is to realise a naturalistic communication between humans and machines. Machines that are able to sense social attitudes like agreement and disagreement and respond to them in a meaningful way are likely to be welcomed by users due to the more natural, efficient and human–centered interaction they are bound to experience. This paper surveys the nonverbal cues that could be present during agreement and disagreement behavioural displays and lists a number of tools that could be useful in detecting them, as well as a few publicly available databases that could be used to train these tools for analysis of spontaneous, audiovisual instances of agreement and disagreement
Action Recognition by Hierarchical Mid-level Action Elements
Realistic videos of human actions exhibit rich spatiotemporal structures at
multiple levels of granularity: an action can always be decomposed into
multiple finer-grained elements in both space and time. To capture this
intuition, we propose to represent videos by a hierarchy of mid-level action
elements (MAEs), where each MAE corresponds to an action-related spatiotemporal
segment in the video. We introduce an unsupervised method to generate this
representation from videos. Our method is capable of distinguishing
action-related segments from background segments and representing actions at
multiple spatiotemporal resolutions. Given a set of spatiotemporal segments
generated from the training data, we introduce a discriminative clustering
algorithm that automatically discovers MAEs at multiple levels of granularity.
We develop structured models that capture a rich set of spatial, temporal and
hierarchical relations among the segments, where the action label and multiple
levels of MAE labels are jointly inferred. The proposed model achieves
state-of-the-art performance in multiple action recognition benchmarks.
Moreover, we demonstrate the effectiveness of our model in real-world
applications such as action recognition in large-scale untrimmed videos and
action parsing
Beat-Event Detection in Action Movie Franchises
While important advances were recently made towards temporally localizing and
recognizing specific human actions or activities in videos, efficient detection
and classification of long video chunks belonging to semantically defined
categories such as "pursuit" or "romance" remains challenging.We introduce a
new dataset, Action Movie Franchises, consisting of a collection of Hollywood
action movie franchises. We define 11 non-exclusive semantic categories -
called beat-categories - that are broad enough to cover most of the movie
footage. The corresponding beat-events are annotated as groups of video shots,
possibly overlapping.We propose an approach for localizing beat-events based on
classifying shots into beat-categories and learning the temporal constraints
between shots. We show that temporal constraints significantly improve the
classification performance. We set up an evaluation protocol for beat-event
localization as well as for shot classification, depending on whether movies
from the same franchise are present or not in the training data
Learning to track for spatio-temporal action localization
We propose an effective approach for spatio-temporal action localization in
realistic videos. The approach first detects proposals at the frame-level and
scores them with a combination of static and motion CNN features. It then
tracks high-scoring proposals throughout the video using a
tracking-by-detection approach. Our tracker relies simultaneously on
instance-level and class-level detectors. The tracks are scored using a
spatio-temporal motion histogram, a descriptor at the track level, in
combination with the CNN features. Finally, we perform temporal localization of
the action using a sliding-window approach at the track level. We present
experimental results for spatio-temporal localization on the UCF-Sports, J-HMDB
and UCF-101 action localization datasets, where our approach outperforms the
state of the art with a margin of 15%, 7% and 12% respectively in mAP
Action Tubelet Detector for Spatio-Temporal Action Localization
Current state-of-the-art approaches for spatio-temporal action localization
rely on detections at the frame level that are then linked or tracked across
time. In this paper, we leverage the temporal continuity of videos instead of
operating at the frame level. We propose the ACtion Tubelet detector
(ACT-detector) that takes as input a sequence of frames and outputs tubelets,
i.e., sequences of bounding boxes with associated scores. The same way
state-of-the-art object detectors rely on anchor boxes, our ACT-detector is
based on anchor cuboids. We build upon the SSD framework. Convolutional
features are extracted for each frame, while scores and regressions are based
on the temporal stacking of these features, thus exploiting information from a
sequence. Our experimental results show that leveraging sequences of frames
significantly improves detection performance over using individual frames. The
gain of our tubelet detector can be explained by both more accurate scores and
more precise localization. Our ACT-detector outperforms the state-of-the-art
methods for frame-mAP and video-mAP on the J-HMDB and UCF-101 datasets, in
particular at high overlap thresholds.Comment: 9 page
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