Two-Stream Convolutional Network Extracting Effective Spatiotemporal Information for Gait Recognition

2019 ◽  
Author(s):  
Yijun Huang ◽  
Yaling Liang ◽  
Zhisong Han ◽  
Minghui Du
Keyword(s):  
Gait Recognition ◽  
Convolutional Network ◽  
Spatiotemporal Information

scholarly journals Sequential convolutional network for behavioral pattern extraction in gait recognition

2021 ◽  
Author(s):  
Xinnan Ding ◽  
Kejun Wang ◽  
Chenhui Wang ◽  
Tianyi Lan ◽  
Liangliang Liu
Keyword(s):  
Gait Recognition ◽  
Behavioral Pattern ◽  
Pattern Extraction ◽  
Convolutional Network

scholarly journals Gait Recognition Method of Underground Coal Mine Personnel Based on Densely Connected Convolution Network and Stacked Convolutional Autoencoder

Entropy ◽  
2020 ◽  
Vol 22 (6) ◽  
pp. 695
Author(s):  
Xiaoyang Liu ◽  
Jinqiang Liu
Keyword(s):  
Coal Mine ◽  
Feature Fusion ◽  
Gait Recognition ◽  
Gait Pattern ◽  
Similarity Learning ◽  
Recognition Method ◽  
Stream Network ◽  
Underground Coal Mine ◽  
Spatiotemporal Information ◽  
Convolutional Autoencoder

Biological recognition methods often use biological characteristics such as the human face, iris, fingerprint, and palm print; however, such images often become blurred under the limitation of the complex environment of the underground, which leads to low identification rates of underground coal mine personnel. A gait recognition method via similarity learning named Two-Stream neural network (TS-Net) is proposed based on a densely connected convolution network (DenseNet) and stacked convolutional autoencoder (SCAE). The mainstream network based on DenseNet is mainly used to learn the similarity of dynamic deep features containing spatiotemporal information in the gait pattern. The auxiliary stream network based on SCAE is used to learn the similarity of static invariant features containing physiological information. Moreover, a novel feature fusion method is adopted to achieve the fusion and representation of dynamic and static features. The extracted features are robust to angle, clothing, miner hats, waterproof shoes, and carrying conditions. The method was evaluated on the challenging CASIA-B gait dataset and the collected gait dataset of underground coal mine personnel (UCMP-GAIT). Experimental results show that the method is effective and feasible for the gait recognition of underground coal mine personnel. Besides, compared with other gait recognition methods, the recognition accuracy has been significantly improved.

scholarly journals Gaitgraph: Graph Convolutional Network for Skeleton-Based Gait Recognition

2021 ◽  
Author(s):  
Torben Teepe ◽  
Ali Khan ◽  
Johannes Gilg ◽  
Fabian Herzog ◽  
Stefan Hormann ◽  
...  
Keyword(s):  
Gait Recognition ◽  
Convolutional Network

scholarly journals WildGait: Learning Gait Representations from Raw Surveillance Streams

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8387
Author(s):  
Adrian Cosma ◽  
Ion Emilian Radoi
Keyword(s):  
Real World ◽  
Gait Recognition ◽  
Convolutional Network ◽  
Privacy Concerns ◽  
Learning Framework ◽  
Non Invasive ◽  
Current State ◽  
Single Person ◽  
Straight Line ◽  
Spatio Temporal

The use of gait for person identification has important advantages such as being non-invasive, unobtrusive, not requiring cooperation and being less likely to be obscured compared to other biometrics. Existing methods for gait recognition require cooperative gait scenarios, in which a single person is walking multiple times in a straight line in front of a camera. We address the challenges of real-world scenarios in which camera feeds capture multiple people, who in most cases pass in front of the camera only once. We address privacy concerns by using only motion information of walking individuals, with no identifiable appearance-based information. As such, we propose a self-supervised learning framework, WildGait, which consists of pre-training a Spatio-Temporal Graph Convolutional Network on a large number of automatically annotated skeleton sequences obtained from raw, real-world surveillance streams to learn useful gait signatures. We collected and compiled the largest pretraining dataset to date of anonymized walking skeletons called Uncooperative Wild Gait, containing over 38k tracklets of anonymized walking 2D skeletons. We make the dataset available to the research community. Our results surpass the current state-of-the-art pose-based gait recognition solutions. Our proposed method is reliable in training gait recognition methods in unconstrained environments, especially in settings with scarce amounts of annotated data.

scholarly journals Deep supervised hashing for gait retrieval

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 1038
Author(s):  
Shohel Sayeed ◽  
Pa Pa Min ◽  
Thian Song Ong
Keyword(s):  
Loss Function ◽  
Large Scale ◽  
Promising Result ◽  
Gait Recognition ◽  
Feature Representation ◽  
Surveillance Systems ◽  
Convolutional Network ◽  
Gait Features ◽  
Retrieval Problem ◽  
Hash Codes

Background: Gait recognition is perceived as the most promising biometric approach for future decades especially because of its efficient applicability in surveillance systems. Due to recent growth in the use of gait biometrics across surveillance systems, the ability to rapidly search for the required data has become an emerging need. Therefore, we addressed the gait retrieval problem, which retrieves people with gaits similar to a query subject from a large-scale dataset. Methods: This paper presents the deep gait retrieval hashing (DGRH) model to address the gait retrieval problem for large-scale datasets. Our proposed method is based on a supervised hashing method with a deep convolutional network. We use the ability of the convolutional neural network (CNN) to capture the semantic gait features for feature representation and learn the compact hash codes with the compatible hash function. Therefore, our DGRH model combines gait feature learning with binary hash codes. In addition, the learning loss is designed with a classification loss function that learns to preserve similarity and a quantization loss function that controls the quality of the hash codes Results: The proposed method was evaluated against the CASIA-B, OUISIR-LP, and OUISIR-MVLP benchmark datasets and received the promising result for gait retrieval tasks. Conclusions: The end-to-end deep supervised hashing model is able to learn discriminative gait features and is efficient in terms of the storage memory and speed for gait retrieval.

Toward understanding the limits of gait recognition

2004 ◽  
Author(s):  
Zongyi Liu ◽  
Laura Malave ◽  
Adebola Osuntogun ◽  
Preksha Sudhakar ◽  
Sudeep Sarkar
Keyword(s):  
Gait Recognition

scholarly journals Knowledge and Geo-Object Based Graph Convolutional Network for Remote Sensing Semantic Segmentation

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3848
Author(s):  
Wei Cui ◽  
Meng Yao ◽  
Yuanjie Hao ◽  
Ziwei Wang ◽  
Xin He ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Prior Knowledge ◽  
Contextual Information ◽  
Information Aggregation ◽  
Semantic Segmentation ◽  
Spatial Correlations ◽  
Convolutional Network ◽  
Object Based ◽  
Graph Neural Networks ◽  
Salt And Pepper

Pixel-based semantic segmentation models fail to effectively express geographic objects and their topological relationships. Therefore, in semantic segmentation of remote sensing images, these models fail to avoid salt-and-pepper effects and cannot achieve high accuracy either. To solve these problems, object-based models such as graph neural networks (GNNs) are considered. However, traditional GNNs directly use similarity or spatial correlations between nodes to aggregate nodes’ information, which rely too much on the contextual information of the sample. The contextual information of the sample is often distorted, which results in a reduction in the node classification accuracy. To solve this problem, a knowledge and geo-object-based graph convolutional network (KGGCN) is proposed. The KGGCN uses superpixel blocks as nodes of the graph network and combines prior knowledge with spatial correlations during information aggregation. By incorporating the prior knowledge obtained from all samples of the study area, the receptive field of the node is extended from its sample context to the study area. Thus, the distortion of the sample context is overcome effectively. Experiments demonstrate that our model is improved by 3.7% compared with the baseline model named Cluster GCN and 4.1% compared with U-Net.

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