EV-Gait: Event-Based Robust Gait Recognition Using Dynamic Vision Sensors

Wang, Yanxiang; Du, Bowen; Shen, Yiran; Wu, Kai; Zhao, Guangrong; Sun, Jianguo; Wen, Hongkai

doi:10.1109/cvpr.2019.00652

Cited by 124 publications

(68 citation statements)

References 34 publications

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“…This approach involves five phases: visualization of the event stream, human figure detection, estimation of optical flow, human pose estimation, and gait recognition based on neural features. EV-Gait [ 19 ] collected several gait datasets using NVS and trains a convolutional neural network (CNN) to tackle the gait recognition problem. EV-Gait-GCN [ 20 ] applied a graph convolutional network (GCN) to identify human by the gait.…”

Section: Related Workmentioning

confidence: 99%

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Event Encryption for Neuromorphic Vision Sensors: Framework, Algorithm, and Evaluation

Wang

et al. 2021

Sensors

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Nowadays, our lives have benefited from various vision-based applications, such as video surveillance, human identification and aided driving. Unauthorized access to the vision-related data greatly threatens users’ privacy, and many encryption schemes have been proposed to secure images and videos in those conventional scenarios. Neuromorphic vision sensor (NVS) is a brand new kind of bio-inspired sensor that can generate a stream of impulse-like events rather than synchronized image frames, which reduces the sensor’s latency and broadens the applications in surveillance and identification. However, the privacy issue related to NVS remains a significant challenge. For example, some image reconstruction and human identification approaches may expose privacy-related information from NVS events. This work is the first to investigate the privacy of NVS. We firstly analyze the possible security attacks to NVS, including grayscale image reconstruction and privacy-related classification. We then propose a dedicated encryption framework for NVS, which incorporates a 2D chaotic mapping to scramble the positions of events and flip their polarities. In addition, an updating score has been designed for controlling the frequency of execution, which supports efficient encryption on different platforms. Finally, extensive experiments have demonstrated that the proposed encryption framework can effectively protect NVS events against grayscale image reconstruction and human identification, and meanwhile, achieve high efficiency on various platforms including resource-constrained devices.

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Section: Related Workmentioning

confidence: 99%

“…Concretely, E2VID [ 12 ] is utilized to carry out the visualization attack, while EV-Gait (-IMG) [ 19 ] and EV-Gait-3DGraph [ 20 ] are used to perform the recognition attack. E2VID provides a well-trained end-to-end neural network to reconstruct images from a stream of events.…”

Section: Nvs and Privacy Challengesmentioning

confidence: 99%

Event Encryption for Neuromorphic Vision Sensors: Framework, Algorithm, and Evaluation

Wang

et al. 2021

Sensors

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“…In gait recognition, the system authenticates or classifies the target person by using her/his walking manner. The used gait data mainly include the following four modalities: camera image gait data [ 4 ], inertial acceleration sensor data [ 5 ], floor sensor data, and passive wireless signals [ 6 ] or wave radar data [ 7 ]. In addition, the recognition performance can be further improved by integrating the above four data modalities [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Gait Recognition Using Optical Motion Capture: A Decision Fusion Based Method

Wang

Xiong

et al. 2021

Sensors

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Human identification based on motion capture data has received signification attentions for its wide applications in authentication and surveillance systems. The optical motion capture system (OMCS) can dynamically capture the high-precision three-dimensional locations of optical trackers that are implemented on a human body, but its potential in applications on gait recognition has not been studied in existing works. On the other hand, a typical OMCS can only support one player one time, which limits its capability and efficiency. In this paper, our goals are investigating the performance of OMCS-based gait recognition performance, and realizing gait recognition in OMCS such that it can support multiple players at the same time. We develop a gait recognition method based on decision fusion, and it includes the following four steps: feature extraction, unreliable feature calibration, classification of single motion frame, and decision fusion of multiple motion frame. We use kernel extreme learning machine (KELM) for single motion classification, and in particular we propose a reliability weighted sum (RWS) decision fusion method to combine the fuzzy decisions of the motion frames. We demonstrate the performance of the proposed method by using walking gait data collected from 76 participants, and results show that KELM significantly outperforms support vector machine (SVM) and random forest in the single motion frame classification task, and demonstrate that the proposed RWS decision fusion rule can achieve better fusion accuracy compared with conventional fusion rules. Our results also show that, with 10 motion trackers that are implemented on lower body locations, the proposed method can achieve 100% validation accuracy with less than 50 gait motion frames.

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“…Castro et al [15] used a mixture of raw frames, optical, and depth information. Wang et al [16] used the information of dynamic vision sensors. Zhang et al [17] united GEIs, skeleton and RGB frame features to enhance robustness.…”

Section: Introductionmentioning

confidence: 99%

Gait Identification Based on Human Skeleton with Pairwise Graph Convolutional Network

Jiang

Sun

2021

2021 IEEE International Conference on Multimedia and Expo (ICME)

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Vision based gait identification is an important research content in the field of biometrics. Most existing gait identification methods extract representations from gait videos and identify a probe gait by ranking the similarities between the probe gait and all the gallery gaits. Since human body skeletons convey significant static and dynamic information of gait, in this paper, an end-to-end gait identification network named Pairwise Graph Convolutional Network (PGCN) is proposed to capture gait feature from skeletons. The skeleton sequences are first mapped into gait graphs and the PGCN is constructed to learn graph representations. The proposed method is examined on CASIA-B dataset on identical-view and cross-dress cases. The contributions of our work are: 1) The PGCN gait identification model is proposed to extract robust gait representation from skeleton sequences. 2) Different fusion structures are compared to explore the best fusion strategy for gait representation. 3) Using skeleton data, our work outperforms previous methods on identical-view and cross-dress cases.

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EV-Gait: Event-Based Robust Gait Recognition Using Dynamic Vision Sensors

Cited by 124 publications

References 34 publications

Event Encryption for Neuromorphic Vision Sensors: Framework, Algorithm, and Evaluation

Event Encryption for Neuromorphic Vision Sensors: Framework, Algorithm, and Evaluation

Gait Recognition Using Optical Motion Capture: A Decision Fusion Based Method

Gait Identification Based on Human Skeleton with Pairwise Graph Convolutional Network

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