- Multimedia University, Faculty of Computing and Informatics, Graduate Studentadd
It is a great challenge to perform high level recognition tasks on videos that are poor in quality. In this paper, we propose a new spatio-temporal mid-level (STEM) feature bank for recognizing human actions in low quality videos. The... more
It is a great challenge to perform high level recognition tasks on videos that are poor in quality. In this paper, we propose a new spatio-temporal mid-level (STEM) feature bank for recognizing human actions in low quality videos. The feature bank comprises of a trio of local spatio-temporal features, i.e. shape, motion and textures, which respectively encode structural , dynamic and statistical information in video. These features are encoded into mid-level representations and ag-gregated to construct STEM. Based on the recent binarized statistical image feature (BSIF), we also design a new spatio-temporal textural feature that extracts discriminately from 3D salient patches. Extensive experiments on the poor quality versions/subsets of the KTH and HMDB51 datasets demonstrate the effectiveness of the proposed approach.
Research Interests:
Human action recognition is a well researched problem, which is considerably more challenging when video quality is poor. In this paper , we investigate human action recognition in low quality videos by leveraging the robustness of... more
Human action recognition is a well researched problem, which is considerably more challenging when video quality is poor. In this paper , we investigate human action recognition in low quality videos by leveraging the robustness of textural features to better characterize actions , instead of relying on shape and motion features may fail under noisy conditions. To accommodate videos, texture descriptors are extended to three orthogonal planes (TOP) to extract spatio-temporal features. Extensive experiments were conducted on low quality versions of the KTH and HMDB51 datasets to evaluate the performance of our proposed approaches against standard baselines. Experimental results and further analysis demonstrated the usefulness of textural features in improving the capability of recognizing human actions from low quality videos.
Research Interests:
Human activity recognition is one of the most intensively studied areas of computer vision and pattern recognition in recent years. A wide variety of approaches have shown to work well against challenging image variations such as... more
Human activity recognition is one of the most intensively studied areas of computer vision and pattern recognition in recent years. A wide variety of approaches have shown to work well against challenging image variations such as appearance, pose and illumination. However, the problem of low video quality remains an unexplored and challenging issue in real-world applications. In this paper, we investigate the effects of low video quality in human action recognition from two perspectives: videos that are poorly sampled spatially (low resolution) and temporally (low frame rate), and compressed videos affected by motion blurring and artifacts. In order to increase the robustness of feature representation under these conditions, we propose the usage of textural features to complement the popular shape and motion features. Extensive experiments were carried out on two well-known benchmark datasets of contrasting nature: the classic KTH dataset and the large-scale HMDB51 dataset. Results obtained with two popular representation schemes (Bag-of-Words, Fisher Vectors) further validate the effectiveness of the proposed approach.
Research Interests:
Shape, motion and texture features have recently gained much popularity in their use for human action recognition. While many of these descriptors have been shown to work well against challenging variations such as appearance, pose and... more
Shape, motion and texture features have recently
gained much popularity in their use for human action recognition.
While many of these descriptors have been shown to
work well against challenging variations such as appearance,
pose and illumination, the problem of low video quality is
relatively unexplored. In this paper, we propose a new idea
of jointly employing these three features within a standard
bag-of-features framework to recognize actions in low quality
videos. The performance of these features were extensively
evaluated and analyzed under three spatial downsampling and
three temporal downsampling modes. Experiments conducted
on the KTH and Weizmann datasets with several combination
of features and settings showed the importance of all three
features (HOG, HOF, LBP-TOP), and how low quality videos
can benefit from the robustness of textural features
gained much popularity in their use for human action recognition.
While many of these descriptors have been shown to
work well against challenging variations such as appearance,
pose and illumination, the problem of low video quality is
relatively unexplored. In this paper, we propose a new idea
of jointly employing these three features within a standard
bag-of-features framework to recognize actions in low quality
videos. The performance of these features were extensively
evaluated and analyzed under three spatial downsampling and
three temporal downsampling modes. Experiments conducted
on the KTH and Weizmann datasets with several combination
of features and settings showed the importance of all three
features (HOG, HOF, LBP-TOP), and how low quality videos
can benefit from the robustness of textural features
Research Interests:
Shape, motion and texture features have recently gained much popularity in their use for human action recognition. While many of these descriptors have been shown to work well against challenging variations such as appearance, pose and... more
Shape, motion and texture features have recently gained much popularity in their use for human action recognition. While many of these descriptors have been shown to work well against challenging variations such as appearance, pose and illumination, the problem of low video quality is relatively unexplored. In this paper, we propose a new idea of jointly employing these three features within a standard bag-of-features framework to recognize actions in low quality
videos. The performance of these features were extensively
evaluated and analyzed under three spatial downsampling and three temporal downsampling modes. Experiments conducted on the KTH and Weizmann datasets with several combination of features and settings showed the importance of all three features (HOG, HOF, LBP-TOP), and how low quality videos can benefit from the robustness of textural features.
videos. The performance of these features were extensively
evaluated and analyzed under three spatial downsampling and three temporal downsampling modes. Experiments conducted on the KTH and Weizmann datasets with several combination of features and settings showed the importance of all three features (HOG, HOF, LBP-TOP), and how low quality videos can benefit from the robustness of textural features.
Research Interests:
Image segmentation is a very important topic in computer vision over the decades. Image segmentation refers to the extracting features from the image used for image analysis, image interpretation and image... more
Image segmentation is a very important topic in computer vision over the
decades. Image segmentation refers to the extracting features from the image
used for image analysis, image interpretation and image understanding. In
order to analyze the images it d
ivides an image into multi
-
regions. This paper
critically analyzed different segmentation methods i.e. thresholding, edge
based segmentation, fuzzy theory based segmentation, region based
segmentation, clustering etc
decades. Image segmentation refers to the extracting features from the image
used for image analysis, image interpretation and image understanding. In
order to analyze the images it d
ivides an image into multi
-
regions. This paper
critically analyzed different segmentation methods i.e. thresholding, edge
based segmentation, fuzzy theory based segmentation, region based
segmentation, clustering etc
Research Interests:
Cloud computing is a hot topic in current computing world. Cloud computing is comes with various features which makes new possibilities for different organizations. Among a lot of challenges faced by cloud users and providers security... more
Cloud computing is a hot topic in current computing world. Cloud computing is comes with various features which makes new possibilities for different organizations. Among a lot of
challenges faced by cloud users and providers security concerns is one of the major issue. The
growth of cloud computing is challenged by the security issues. In this paper we have analyzed several issues in cloud computing environment. Several solutions were proposed to minimize the existing issues in cloud computing. This paper introduces some analysis of existing solutions which can be a motivation for development of trusted solutions in cloud environment.
challenges faced by cloud users and providers security concerns is one of the major issue. The
growth of cloud computing is challenged by the security issues. In this paper we have analyzed several issues in cloud computing environment. Several solutions were proposed to minimize the existing issues in cloud computing. This paper introduces some analysis of existing solutions which can be a motivation for development of trusted solutions in cloud environment.
Research Interests:
The nonlinear scattering effects in optical fiber occur due to inelastic-scattering of a photon to a lower energy photon. This review presents the Stimulated Raman Scattering and some of its applications in fiber... more
The nonlinear scattering effects in optical fiber occur due to
inelastic-scattering of a photon to a lower energy photon. This
review presents the Stimulated Raman Scattering and some of its
applications in fiber optic communications.
inelastic-scattering of a photon to a lower energy photon. This
review presents the Stimulated Raman Scattering and some of its
applications in fiber optic communications.