28,603 research outputs found
UG^2: a Video Benchmark for Assessing the Impact of Image Restoration and Enhancement on Automatic Visual Recognition
Advances in image restoration and enhancement techniques have led to
discussion about how such algorithmscan be applied as a pre-processing step to
improve automatic visual recognition. In principle, techniques like deblurring
and super-resolution should yield improvements by de-emphasizing noise and
increasing signal in an input image. But the historically divergent goals of
the computational photography and visual recognition communities have created a
significant need for more work in this direction. To facilitate new research,
we introduce a new benchmark dataset called UG^2, which contains three
difficult real-world scenarios: uncontrolled videos taken by UAVs and manned
gliders, as well as controlled videos taken on the ground. Over 160,000
annotated frames forhundreds of ImageNet classes are available, which are used
for baseline experiments that assess the impact of known and unknown image
artifacts and other conditions on common deep learning-based object
classification approaches. Further, current image restoration and enhancement
techniques are evaluated by determining whether or not theyimprove baseline
classification performance. Results showthat there is plenty of room for
algorithmic innovation, making this dataset a useful tool going forward.Comment: Supplemental material: https://goo.gl/vVM1xe, Dataset:
https://goo.gl/AjA6En, CVPR 2018 Prize Challenge: ug2challenge.or
TagBook: A Semantic Video Representation without Supervision for Event Detection
We consider the problem of event detection in video for scenarios where only
few, or even zero examples are available for training. For this challenging
setting, the prevailing solutions in the literature rely on a semantic video
representation obtained from thousands of pre-trained concept detectors.
Different from existing work, we propose a new semantic video representation
that is based on freely available social tagged videos only, without the need
for training any intermediate concept detectors. We introduce a simple
algorithm that propagates tags from a video's nearest neighbors, similar in
spirit to the ones used for image retrieval, but redesign it for video event
detection by including video source set refinement and varying the video tag
assignment. We call our approach TagBook and study its construction,
descriptiveness and detection performance on the TRECVID 2013 and 2014
multimedia event detection datasets and the Columbia Consumer Video dataset.
Despite its simple nature, the proposed TagBook video representation is
remarkably effective for few-example and zero-example event detection, even
outperforming very recent state-of-the-art alternatives building on supervised
representations.Comment: accepted for publication as a regular paper in the IEEE Transactions
on Multimedi
Video semantic content analysis framework based on ontology combined MPEG-7
The rapid increase in the available amount of video data is creating a growing demand for efficient methods for understanding and managing it at the semantic level. New multimedia standard, MPEG-7, provides the rich functionalities to enable the generation of audiovisual descriptions and is expressed solely in XML Schema which provides little support for expressing semantic knowledge. In this paper, a video semantic content analysis framework based on ontology combined MPEG-7 is presented. Domain
ontology is used to define high level semantic concepts and their relations in the context of the examined domain. MPEG-7 metadata terms of audiovisual descriptions and video content analysis algorithms are expressed in this ontology to enrich video semantic analysis. OWL is used for the ontology description. Rules in Description Logic are defined to describe how low-level features and algorithms for video analysis should be applied according to different perception content. Temporal Description Logic is used to describe the
semantic events, and a reasoning algorithm is proposed for events detection. The proposed framework is demonstrated in sports video domain and shows promising results
LOMo: Latent Ordinal Model for Facial Analysis in Videos
We study the problem of facial analysis in videos. We propose a novel weakly
supervised learning method that models the video event (expression, pain etc.)
as a sequence of automatically mined, discriminative sub-events (eg. onset and
offset phase for smile, brow lower and cheek raise for pain). The proposed
model is inspired by the recent works on Multiple Instance Learning and latent
SVM/HCRF- it extends such frameworks to model the ordinal or temporal aspect in
the videos, approximately. We obtain consistent improvements over relevant
competitive baselines on four challenging and publicly available video based
facial analysis datasets for prediction of expression, clinical pain and intent
in dyadic conversations. In combination with complimentary features, we report
state-of-the-art results on these datasets.Comment: 2016 IEEE Conference on Computer Vision and Pattern Recognition
(CVPR
Rate-Accuracy Trade-Off In Video Classification With Deep Convolutional Neural Networks
Advanced video classification systems decode video frames to derive the
necessary texture and motion representations for ingestion and analysis by
spatio-temporal deep convolutional neural networks (CNNs). However, when
considering visual Internet-of-Things applications, surveillance systems and
semantic crawlers of large video repositories, the video capture and the
CNN-based semantic analysis parts do not tend to be co-located. This
necessitates the transport of compressed video over networks and incurs
significant overhead in bandwidth and energy consumption, thereby significantly
undermining the deployment potential of such systems. In this paper, we
investigate the trade-off between the encoding bitrate and the achievable
accuracy of CNN-based video classification models that directly ingest
AVC/H.264 and HEVC encoded videos. Instead of retaining entire compressed video
bitstreams and applying complex optical flow calculations prior to CNN
processing, we only retain motion vector and select texture information at
significantly-reduced bitrates and apply no additional processing prior to CNN
ingestion. Based on three CNN architectures and two action recognition
datasets, we achieve 11%-94% saving in bitrate with marginal effect on
classification accuracy. A model-based selection between multiple CNNs
increases these savings further, to the point where, if up to 7% loss of
accuracy can be tolerated, video classification can take place with as little
as 3 kbps for the transport of the required compressed video information to the
system implementing the CNN models
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