Efficient and effective human action recognition in video through motion boundary description with a compact set of trajectories

Human action recognition (HAR) is at the core of human-computer interaction and video scene understanding. However, achieving effective HAR in an unconstrained environment is still a challenging task. To that end, trajectory-based video representations are currently widely used. Despite the promising levels of effectiveness achieved by these approaches, problems regarding computational complexity and the presence of redundant trajectories still need to be addressed in a satisfactory way. In this paper, we propose a method for trajectory rejection, reducing the number of redundant trajectories without degrading the effectiveness of HAR. Furthermore, to realize efficient optical flow estimation prior to trajectory extraction, we integrate a method for dynamic frame skipping. Experiments with four publicly available human action datasets show that the proposed approach outperforms state-of-the-art HAR approaches in terms of effectiveness, while simultaneously mitigating the computational complexity

Sub-sampled dictionaries for coarse-to-fine sparse representation-based human action recognition

Automatic human action recognition is a core functionality of systems for video surveillance and human-object interaction. However, the diverse nature of human actions and the noisy nature of most video content make it difficult to achieve effective human action recognition. To overcome the aforementioned problems, Sparse Representation (SR) has recently attracted substantial research attention. However, although SR-based approaches have proven to be reasonably effective, the computational complexity of the testing stage prohibits their usage by applications requiring support for real-time operation and a vast number of human action classes. In this paper, we propose a novel method for human action recognition, leveraging coarse-to-fine sparse representations that have been obtained through dictionary sub-sampling. Comparative experimental results obtained for the UCF50 dataset demonstrate that the proposed method is able to achieve efficient human action recognition, at no substantial loss in recognition accuracy

Fimasartan Ameliorates Nonalcoholic Fatty Liver Disease through PPAR δ

To investigate the effects of fimasartan on nonalcoholic fatty liver disease in hyperlipidemic and hypertensive conditions, the levels of biomarkers related to fatty acid metabolism were determined in HepG2 and differentiated 3T3-L1 cells treated by high fatty acid and liver and visceral fat tissue samples of spontaneously hypertensive rats (SHRs) given high-fat diet. In HepG2 cells and liver tissues, fimasartan was shown to increase the protein levels of peroxisome proliferator-activated receptor delta (PPARδ), phosphorylated 5′ adenosine monophosphate-activated protein kinase (p-AMPK), phosphorylated acetyl-CoA carboxylase (p-ACC), malonyl-CoA decarboxylase (MCD), medium chain acyl-CoA dehydrogenase (MCAD), and peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PGC-1α), and it led to a decrease in the protein levels of 11 beta-hydroxysteroid dehydrogenase 1 (11β-HSDH1), fatty acid synthase (FAS), and tumor necrosis factor-alpha (TNF-α). Fimasartan decreased lipid contents in HepG2 and differentiated 3T3-L1 cells and liver tissues. In addition, fimasartan increased the adiponectin level in visceral fat tissues. The antiadipogenic effects of fimasartan were offset by PPARδ antagonist (GSK0660). Consequently, fimasartan ameliorates nonalcoholic fatty liver disease mainly through the activation of oxidative metabolism represented by PPARδ-AMPK-PGC-1α pathway

Effective and efficient human action recognition using dynamic frame skipping and trajectory rejection

Human action recognition (HAR) is a core technology for human-computer interaction and video understanding, attracting significant research and development attention in the field of computer vision. However, in uncontrolled environments, achieving effective HAR is still challenging, due to the widely varying nature of video content. In previous research efforts, trajectory-based video representations have been widely used for HAR. Although these approaches show state-of-the-art HAR performance for various datasets, issues like a high computational complexity and the presence of redundant trajectories still need to be addressed in order to solve the problem of real-world HAR. In this paper, we propose a novel method for HAR, integrating a technique for rejecting redundant trajectories that are mainly originating from camera movement, without degrading the effectiveness of HAR. Furthermore, in order to facilitate efficient optical flow estimation prior to trajectory extraction, we integrate a technique for dynamic frame skipping. As a result, we only make use of a small subset of the frames present in a video clip for optical flow estimation. Comparative experiments with five publicly available human action datasets show that the proposed method outperforms state-of-the-art HAR approaches in terms of effectiveness, while simultaneously mitigating the computational complexity. (C) 2016 Elsevier B.V. All rights reserved