2,311 research outputs found
Activity Recognition based on a Magnitude-Orientation Stream Network
The temporal component of videos provides an important clue for activity
recognition, as a number of activities can be reliably recognized based on the
motion information. In view of that, this work proposes a novel temporal stream
for two-stream convolutional networks based on images computed from the optical
flow magnitude and orientation, named Magnitude-Orientation Stream (MOS), to
learn the motion in a better and richer manner. Our method applies simple
nonlinear transformations on the vertical and horizontal components of the
optical flow to generate input images for the temporal stream. Experimental
results, carried on two well-known datasets (HMDB51 and UCF101), demonstrate
that using our proposed temporal stream as input to existing neural network
architectures can improve their performance for activity recognition. Results
demonstrate that our temporal stream provides complementary information able to
improve the classical two-stream methods, indicating the suitability of our
approach to be used as a temporal video representation.Comment: 8 pages, SIBGRAPI 201
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
Spatial and Temporal Mutual Promotion for Video-based Person Re-identification
Video-based person re-identification is a crucial task of matching video
sequences of a person across multiple camera views. Generally, features
directly extracted from a single frame suffer from occlusion, blur,
illumination and posture changes. This leads to false activation or missing
activation in some regions, which corrupts the appearance and motion
representation. How to explore the abundant spatial-temporal information in
video sequences is the key to solve this problem. To this end, we propose a
Refining Recurrent Unit (RRU) that recovers the missing parts and suppresses
noisy parts of the current frame's features by referring historical frames.
With RRU, the quality of each frame's appearance representation is improved.
Then we use the Spatial-Temporal clues Integration Module (STIM) to mine the
spatial-temporal information from those upgraded features. Meanwhile, the
multi-level training objective is used to enhance the capability of RRU and
STIM. Through the cooperation of those modules, the spatial and temporal
features mutually promote each other and the final spatial-temporal feature
representation is more discriminative and robust. Extensive experiments are
conducted on three challenging datasets, i.e., iLIDS-VID, PRID-2011 and MARS.
The experimental results demonstrate that our approach outperforms existing
state-of-the-art methods of video-based person re-identification on iLIDS-VID
and MARS and achieves favorable results on PRID-2011.Comment: Accepted by AAAI19 as spotligh
Deep temporal motion descriptor (DTMD) for human action recognition
Spatiotemporal features have significant importance in human action recognition, as they provide the actor's shape and motion characteristics specific to each action class. This paper presents a new deep spatiotemporal human action representation, \Deep Temporal Motion Descriptor (DTMD)", which shares the attributes of holistic and deep learned features. To generate the DTMD descriptor, the actor's silhouettes are gathered into single motion templates through applying motion history images. These motion templates capture the spatiotemporal movements of the actor and compactly represents the human actions using a single 2D template. Then, deep convolutional neural networks are used to compute discriminative deep features from motion history templates to produce DTMD. Later, DTMD is used for learn a model to recognise human actions using a softmax classifier. The advantage of DTMD comes from (i) DTMD is automatically learned from videos and contains higher dimensional discriminative spatiotemporal representation as compared to handcrafted features; (ii) DTMD reduces the computational complexity of human activity recognition as all the video frames are compactly represented as a single motion template; (iii) DTMD works e ectively for single and multiview action recognition. We conducted experiments on three challenging datasets: MuHAVI-Uncut, iXMAS, and IAVID-1. The experimental findings reveal that DTMD outperforms previous methods and achieves the highest action prediction rate on the MuHAVI-Uncut datase
VIOLA - A multi-purpose and web-based visualization tool for neuronal-network simulation output
Neuronal network models and corresponding computer simulations are invaluable
tools to aid the interpretation of the relationship between neuron properties,
connectivity and measured activity in cortical tissue. Spatiotemporal patterns
of activity propagating across the cortical surface as observed experimentally
can for example be described by neuronal network models with layered geometry
and distance-dependent connectivity. The interpretation of the resulting stream
of multi-modal and multi-dimensional simulation data calls for integrating
interactive visualization steps into existing simulation-analysis workflows.
Here, we present a set of interactive visualization concepts called views for
the visual analysis of activity data in topological network models, and a
corresponding reference implementation VIOLA (VIsualization Of Layer Activity).
The software is a lightweight, open-source, web-based and platform-independent
application combining and adapting modern interactive visualization paradigms,
such as coordinated multiple views, for massively parallel neurophysiological
data. For a use-case demonstration we consider spiking activity data of a
two-population, layered point-neuron network model subject to a spatially
confined excitation originating from an external population. With the multiple
coordinated views, an explorative and qualitative assessment of the
spatiotemporal features of neuronal activity can be performed upfront of a
detailed quantitative data analysis of specific aspects of the data.
Furthermore, ongoing efforts including the European Human Brain Project aim at
providing online user portals for integrated model development, simulation,
analysis and provenance tracking, wherein interactive visual analysis tools are
one component. Browser-compatible, web-technology based solutions are therefore
required. Within this scope, with VIOLA we provide a first prototype.Comment: 38 pages, 10 figures, 3 table
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