817 research outputs found
Exploiting Image-trained CNN Architectures for Unconstrained Video Classification
We conduct an in-depth exploration of different strategies for doing event
detection in videos using convolutional neural networks (CNNs) trained for
image classification. We study different ways of performing spatial and
temporal pooling, feature normalization, choice of CNN layers as well as choice
of classifiers. Making judicious choices along these dimensions led to a very
significant increase in performance over more naive approaches that have been
used till now. We evaluate our approach on the challenging TRECVID MED'14
dataset with two popular CNN architectures pretrained on ImageNet. On this
MED'14 dataset, our methods, based entirely on image-trained CNN features, can
outperform several state-of-the-art non-CNN models. Our proposed late fusion of
CNN- and motion-based features can further increase the mean average precision
(mAP) on MED'14 from 34.95% to 38.74%. The fusion approach achieves the
state-of-the-art classification performance on the challenging UCF-101 dataset
New Hybrid Deep Learning Method to Recognize Human Action from Video
There has been a tremendous increase in internet users and enough bandwidth in recent years. Because Internet connectivity is so inexpensive, information sharing (text, audio, and video) has become more popular and faster. This video content must be examined in order to classify it for different purposes for users. Several machine learning approaches for video classification have been developed to save users time and energy. The use of deep neural networks to recognize human behavior has become a popular issue in recent years. Although significant progress has been made in the field of video recognition, there are still numerous challenges in the realm of video to be overcome. Convolutional neural networks (CNNs) are well-known for requiring a fixed-size image input, which limits the network topology and reduces identification accuracy. Despite the fact that this problem has been solved in the world of photos, it has yet to be solved in the area of video. We present a ten stacked three-dimensional (3D) convolutional network based on the spatial pyramid-based pooling to handle the input problem of fixed size video frames in video recognition. The network structure is made up of three sections, as the name suggests: a ten-layer stacked 3DCNN, DenseNet, and SPPNet. A KTH dataset was used to test our algorithms. The experimental findings showed that our model outperformed existing models in the area of video-based behavior identification by 2% margin accuracy
DeepProposals: Hunting Objects and Actions by Cascading Deep Convolutional Layers
In this paper, a new method for generating object and action proposals in
images and videos is proposed. It builds on activations of different
convolutional layers of a pretrained CNN, combining the localization accuracy
of the early layers with the high informative-ness (and hence recall) of the
later layers. To this end, we build an inverse cascade that, going backward
from the later to the earlier convolutional layers of the CNN, selects the most
promising locations and refines them in a coarse-to-fine manner. The method is
efficient, because i) it re-uses the same features extracted for detection, ii)
it aggregates features using integral images, and iii) it avoids a dense
evaluation of the proposals thanks to the use of the inverse coarse-to-fine
cascade. The method is also accurate. We show that our DeepProposals outperform
most of the previously proposed object proposal and action proposal approaches
and, when plugged into a CNN-based object detector, produce state-of-the-art
detection performance.Comment: 15 page
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