317 research outputs found
A Hybrid Graph Network for Complex Activity Detection in Video
Interpretation and understanding of video presents a challenging computer
vision task in numerous fields - e.g. autonomous driving and sports analytics.
Existing approaches to interpreting the actions taking place within a video
clip are based upon Temporal Action Localisation (TAL), which typically
identifies short-term actions. The emerging field of Complex Activity Detection
(CompAD) extends this analysis to long-term activities, with a deeper
understanding obtained by modelling the internal structure of a complex
activity taking place within the video. We address the CompAD problem using a
hybrid graph neural network which combines attention applied to a graph
encoding the local (short-term) dynamic scene with a temporal graph modelling
the overall long-duration activity. Our approach is as follows: i) Firstly, we
propose a novel feature extraction technique which, for each video snippet,
generates spatiotemporal `tubes' for the active elements (`agents') in the
(local) scene by detecting individual objects, tracking them and then
extracting 3D features from all the agent tubes as well as the overall scene.
ii) Next, we construct a local scene graph where each node (representing either
an agent tube or the scene) is connected to all other nodes. Attention is then
applied to this graph to obtain an overall representation of the local dynamic
scene. iii) Finally, all local scene graph representations are interconnected
via a temporal graph, to estimate the complex activity class together with its
start and end time. The proposed framework outperforms all previous
state-of-the-art methods on all three datasets including ActivityNet-1.3,
Thumos-14, and ROAD.Comment: This paper is Accepted at WACV 202
Recommended from our members
Classification videos reveal the visual information driving complex real-world speeded decisions
Humans can rapidly discriminate complex scenarios as they unfold in real time, for example during law enforcement or, more prosaically, driving and sport. Such decision-making improves with experience, as new sources of information are exploited. For example, sports experts are able to predict the outcome of their opponent’s next action (e.g. a tennis stroke) based on kinematic cues “read” from preparatory body movements. Here, we explore the use of psychophysical classification-image techniques to reveal how participants interpret complex scenarios. We used sport as a test case, filming tennis players serving and hitting ground strokes, each with two possible directions. These videos were presented to novices and club-level amateurs, running from 0.8 seconds before to 0.2 seconds after racquet-ball contact. During practice, participants anticipated shot direction under a time limit targeting 90% accuracy. Participants then viewed videos through Gaussian windows ("bubbles") placed at random in the temporal, spatial or spatiotemporal domains. Comparing bubbles from correct and incorrect trials revealed how information from different regions contributed toward a correct response. Temporally, only later frames of the videos supported accurate responding (from ~0.05 seconds before ball contact to 0.1+ seconds afterwards). Spatially, information was accrued from the ball’s trajectory and from the opponent’s head. Spatiotemporal bubbles again highlighted ball trajectory information, but seemed susceptible to an attentional cuing artefact, which may caution against their wider use. Overall, bubbles proved effective in revealing regions of information accrual, and could thus be applied to help understand choice behavior in a range of ecologically valid situations
REPRESENTATION LEARNING FOR ACTION RECOGNITION
The objective of this research work is to develop discriminative representations for human
actions. The motivation stems from the fact that there are many issues encountered while
capturing actions in videos like intra-action variations (due to actors, viewpoints, and duration),
inter-action similarity, background motion, and occlusion of actors. Hence, obtaining
a representation which can address all the variations in the same action while maintaining
discrimination with other actions is a challenging task. In literature, actions have been represented
either using either low-level or high-level features. Low-level features describe
the motion and appearance in small spatio-temporal volumes extracted from a video. Due
to the limited space-time volume used for extracting low-level features, they are not able
to account for viewpoint and actor variations or variable length actions. On the other hand,
high-level features handle variations in actors, viewpoints, and duration but the resulting
representation is often high-dimensional which introduces the curse of dimensionality. In
this thesis, we propose new representations for describing actions by combining the advantages
of both low-level and high-level features. Specifically, we investigate various linear
and non-linear decomposition techniques to extract meaningful attributes in both high-level
and low-level features. In the first approach, the sparsity of high-level feature descriptors is leveraged to build
action-specific dictionaries. Each dictionary retains only the discriminative information
for a particular action and hence reduces inter-action similarity. Then, a sparsity-based
classification method is proposed to classify the low-rank representation of clips obtained
using these dictionaries. We show that this representation based on dictionary learning improves
the classification performance across actions. Also, a few of the actions consist of
rapid body deformations that hinder the extraction of local features from body movements.
Hence, we propose to use a dictionary which is trained on convolutional neural network
(CNN) features of the human body in various poses to reliably identify actors from the
background. Particularly, we demonstrate the efficacy of sparse representation in the identification
of the human body under rapid and substantial deformation.
In the first two approaches, sparsity-based representation is developed to improve discriminability
using class-specific dictionaries that utilize action labels. However, developing
an unsupervised representation of actions is more beneficial as it can be used to both
recognize similar actions and localize actions. We propose to exploit inter-action similarity
to train a universal attribute model (UAM) in order to learn action attributes (common and
distinct) implicitly across all the actions. Using maximum aposteriori (MAP) adaptation,
a high-dimensional super action-vector (SAV) for each clip is extracted. As this SAV contains
redundant attributes of all other actions, we use factor analysis to extract a novel lowvi
dimensional action-vector representation for each clip. Action-vectors are shown to suppress
background motion and highlight actions of interest in both trimmed and untrimmed
clips that contributes to action recognition without the help of any classifiers.
It is observed during our experiments that action-vector cannot effectively discriminate
between actions which are visually similar to each other. Hence, we subject action-vectors
to supervised linear embedding using linear discriminant analysis (LDA) and probabilistic
LDA (PLDA) to enforce discrimination. Particularly, we show that leveraging complimentary
information across action-vectors using different local features followed by discriminative
embedding provides the best classification performance. Further, we explore
non-linear embedding of action-vectors using Siamese networks especially for fine-grained
action recognition. A visualization of the hidden layer output in Siamese networks shows
its ability to effectively separate visually similar actions. This leads to better classification
performance than linear embedding on fine-grained action recognition.
All of the above approaches are presented on large unconstrained datasets with hundreds
of examples per action. However, actions in surveillance videos like snatch thefts are
difficult to model because of the diverse variety of scenarios in which they occur and very
few labeled examples. Hence, we propose to utilize the universal attribute model (UAM)
trained on large action datasets to represent such actions. Specifically, we show that there
are similarities between certain actions in the large datasets with snatch thefts which help
in extracting a representation for snatch thefts using the attributes from the UAM. This
representation is shown to be effective in distinguishing snatch thefts from regular actions
with high accuracy.In summary, this thesis proposes both supervised and unsupervised approaches for representing
actions which provide better discrimination than existing representations. The
first approach presents a dictionary learning based sparse representation for effective discrimination
of actions. Also, we propose a sparse representation for the human body based
on dictionaries in order to recognize actions with rapid body deformations. In the next
approach, a low-dimensional representation called action-vector for unsupervised action
recognition is presented. Further, linear and non-linear embedding of action-vectors is
proposed for addressing inter-action similarity and fine-grained action recognition, respectively.
Finally, we propose a representation for locating snatch thefts among thousands of
regular interactions in surveillance videos
- …