scholarly journals Riemannian Spatio-Temporal Features of Locomotion for Individual Recognition

Sensors ◽  
2018 ◽  
Vol 19 (1) ◽  
pp. 56 ◽  
Author(s):  
Jianhai Zhang ◽  
Zhiyong Feng ◽  
Yong Su ◽  
Meng Xing ◽  
Wanli Xue
Keyword(s):  
Riemannian Manifold ◽  
Individual Recognition ◽  
Metric Learning ◽  
Influence Factors ◽  
Feature Representation ◽  
Geometric Feature ◽  
Major Influence ◽  
Temporal Features ◽  
Spatio Temporal ◽  
Spatial State

Individual recognition based on skeletal sequence is a challenging computer vision task with multiple important applications, such as public security, human–computer interaction, and surveillance. However, much of the existing work usually fails to provide any explicit quantitative differences between different individuals. In this paper, we propose a novel 3D spatio-temporal geometric feature representation of locomotion on Riemannian manifold, which explicitly reveals the intrinsic differences between individuals. To this end, we construct mean sequence by aligning related motion sequences on the Riemannian manifold. The differences in respect to this mean sequence are modeled as spatial state descriptors. Subsequently, a temporal hierarchy of covariance are imposed on the state descriptors, making it a higher-order statistical spatio-temporal feature representation, showing unique biometric characteristics for individuals. Finally, we introduce a kernel metric learning method to improve the classification accuracy. We evaluated our method on two public databases: the CMU Mocap database and the UPCV Gait database. Furthermore, we also constructed a new database for evaluating running and analyzing two major influence factors of walking. As a result, the proposed approach achieves promising results in all experiments.

Spatio-temporal metric learning for individual recognition from locomotion

2020 ◽  
Vol 67 ◽  
pp. 102753
Author(s):  
Yong Su ◽  
Simin An ◽  
Zhiyong Feng ◽  
Meng Xing ◽  
Jianhai Zhang
Keyword(s):  
Individual Recognition ◽  
Metric Learning ◽  
Spatio Temporal

scholarly journals Spatio-Temporal Scale Coded Bag-of-Words

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6380
Author(s):  
Divina Govender ◽  
Jules-Raymond Tapamo
Keyword(s):  
Action Recognition ◽  
Low Cost ◽  
Computational Cost ◽  
Temporal Scale ◽  
Feature Representation ◽  
Bag Of Words ◽  
Learning Approaches ◽  
Temporal Features ◽  
Dense Trajectory ◽  
Spatio Temporal

The Bag-of-Words (BoW) framework has been widely used in action recognition tasks due to its compact and efficient feature representation. Various modifications have been made to this framework to increase its classification power. This often results in an increased complexity and reduced efficiency. Inspired by the success of image-based scale coded BoW representations, we propose a spatio-temporal scale coded BoW (SC-BoW) for video-based recognition. This involves encoding extracted multi-scale information into BoW representations by partitioning spatio-temporal features into sub-groups based on the spatial scale from which they were extracted. We evaluate SC-BoW in two experimental setups. We first present a general pipeline to perform real-time action recognition with SC-BoW. Secondly, we apply SC-BoW onto the popular Dense Trajectory feature set. Results showed SC-BoW representations to successfully improve performance by 2–7% with low added computational cost. Notably, SC-BoW on Dense Trajectories outperformed more complex deep learning approaches. Thus, scale coding is a low-cost and low-level encoding scheme that increases classification power of the standard BoW without compromising efficiency.

Fire Detection Based on Fractal Analysis and Spatio-Temporal Features

2021 ◽  
Author(s):  
Monir Torabian ◽  
Hossein Pourghassem ◽  
Homayoun Mahdavi-Nasab
Keyword(s):  
Fractal Analysis ◽  
Fire Detection ◽  
Temporal Features ◽  
Spatio Temporal

Kinematic Adaptive Frequency Sampling Combined Spatio Temporal Features for Snow Monitoring in Aerospace Applications

2021 ◽  
pp. 115472
Author(s):  
Parameshwaran Ramalingam ◽  
Lakshminarayanan Gopalakrishnan ◽  
Manikandan Ramachandran ◽  
Rizwan Patan
Keyword(s):  
Aerospace Applications ◽  
Temporal Features ◽  
Frequency Sampling ◽  
Spatio Temporal

Algicidal and Bactericidal Effect of Potassium Monopersulfate Compound on Eutrophic Water

2014 ◽  
Vol 707 ◽  
pp. 259-262 ◽  
Author(s):  
Ming Song Wu ◽  
Xin Yang Xu ◽  
Xun Xu ◽  
Yue Ting Zeng ◽  
Jing Nan Zhang ◽  
...  
Keyword(s):  
Contact Time ◽  
Influence Factors ◽  
Bactericidal Effect ◽  
Major Influence ◽  
Treatment Efficiency ◽  
Compound A ◽  
Eutrophic Water ◽  
Landscape Water ◽  
Algae And Bacteria

Algae and bacteria blooms in eutrophication in summer have made the quality of landscape water degradation. Treatment efficiency of potassium monopersulfate compound, a new kind of oxidation reagent, on killing algae and bacteria has been valued and the effect of influence factors, such as dosage, contact time and temperature are also discussed. The results indicated that potassium monopersulfate is appropriate for killing algae and bacteria in landscape water, dosage and contact time are the major influence factors. The contact time should be longer than 20min and the algicidal rate is higher when the temperature is above 20°C.

P4-225: Analysis of Spatio-Temporal Features of Histopathology in Murine Alzheimer's Disease Models and Human Alzheimer's Disease Samples

2016 ◽  
Vol 12 ◽  
pp. P1115-P1115
Author(s):  
Vera Niederkofler ◽  
Christina Hoeller ◽  
Joerg Neddens ◽  
Ewald Auer ◽  
Heinrich Roemer ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Disease Models ◽  
Temporal Features ◽  
Spatio Temporal

Predicting Human Mobility with Reinforcement-Learning-Based Long-Term Periodicity Modeling

2021 ◽  
Vol 12 (6) ◽  
pp. 1-23
Author(s):  
Shuo Tao ◽  
Jingang Jiang ◽  
Defu Lian ◽  
Kai Zheng ◽  
Enhong Chen
Keyword(s):  
Reinforcement Learning ◽  
Human Mobility ◽  
Recurrent Network ◽  
Mobility Prediction ◽  
Learning Framework ◽  
Temporal Features ◽  
Wide Range ◽  
Spatio Temporal ◽  
Historical Trajectory

Mobility prediction plays an important role in a wide range of location-based applications and services. However, there are three problems in the existing literature: (1) explicit high-order interactions of spatio-temporal features are not systemically modeled; (2) most existing algorithms place attention mechanisms on top of recurrent network, so they can not allow for full parallelism and are inferior to self-attention for capturing long-range dependence; (3) most literature does not make good use of long-term historical information and do not effectively model the long-term periodicity of users. To this end, we propose MoveNet and RLMoveNet. MoveNet is a self-attention-based sequential model, predicting each user’s next destination based on her most recent visits and historical trajectory. MoveNet first introduces a cross-based learning framework for modeling feature interactions. With self-attention on both the most recent visits and historical trajectory, MoveNet can use an attention mechanism to capture the user’s long-term regularity in a more efficient way. Based on MoveNet, to model long-term periodicity more effectively, we add the reinforcement learning layer and named RLMoveNet. RLMoveNet regards the human mobility prediction as a reinforcement learning problem, using the reinforcement learning layer as the regularization part to drive the model to pay attention to the behavior with periodic actions, which can help us make the algorithm more effective. We evaluate both of them with three real-world mobility datasets. MoveNet outperforms the state-of-the-art mobility predictor by around 10% in terms of accuracy, and simultaneously achieves faster convergence and over 4x training speedup. Moreover, RLMoveNet achieves higher prediction accuracy than MoveNet, which proves that modeling periodicity explicitly from the perspective of reinforcement learning is more effective.

scholarly journals Video-Based Person Re-Identification by an End-To-End Learning Architecture with Hybrid Deep Appearance-Temporal Feature

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3669 ◽  
Author(s):  
Rui Sun ◽  
Qiheng Huang ◽  
Miaomiao Xia ◽  
Jun Zhang
Keyword(s):  
Feature Representation ◽  
Visual Sensor ◽  
Stream Structure ◽  
Temporal Features ◽  
Feature Structure ◽  
End To End ◽  
Global And Local ◽  
Siamese Networks ◽  
Appearance Feature ◽  
Temporal Feature

Video-based person re-identification is an important task with the challenges of lighting variation, low-resolution images, background clutter, occlusion, and human appearance similarity in the multi-camera visual sensor networks. In this paper, we propose a video-based person re-identification method called the end-to-end learning architecture with hybrid deep appearance-temporal feature. It can learn the appearance features of pivotal frames, the temporal features, and the independent distance metric of different features. This architecture consists of two-stream deep feature structure and two Siamese networks. For the first-stream structure, we propose the Two-branch Appearance Feature (TAF) sub-structure to obtain the appearance information of persons, and used one of the two Siamese networks to learn the similarity of appearance features of a pairwise person. To utilize the temporal information, we designed the second-stream structure that consisting of the Optical flow Temporal Feature (OTF) sub-structure and another Siamese network, to learn the person’s temporal features and the distances of pairwise features. In addition, we select the pivotal frames of video as inputs to the Inception-V3 network on the Two-branch Appearance Feature sub-structure, and employ the salience-learning fusion layer to fuse the learned global and local appearance features. Extensive experimental results on the PRID2011, iLIDS-VID, and Motion Analysis and Re-identification Set (MARS) datasets showed that the respective proposed architectures reached 79%, 59% and 72% at Rank-1 and had advantages over state-of-the-art algorithms. Meanwhile, it also improved the feature representation ability of persons.

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