相关论文: Continual Person Identification using Footstep-Ind…
Quantitative estimation of human joint motion in daily living spaces is essential for early detection and rehabilitation tracking of neuromusculoskeletal disorders (e.g., Parkinson's) and mitigating trip and fall risks for older adults.…
Footstep recognition is a relatively new biometric which aims to discriminate people using walking characteristics. There are several feature and technology have been adopted in various research. This study will attempt to show a…
In-home gait analysis is important for providing early diagnosis and adaptive treatments for individuals with gait disorders. Existing systems include wearables and pressure mats, but they have limited scalability. Recent studies have…
An autonomous computer system (such as a robot) typically needs to identify, locate, and track persons appearing in its sight. However, most solutions have their limitations regarding efficiency, practicability, or environmental…
Footfall based biometric system is perhaps the only person identification technique which does not hinder the natural movement of an individual. This is a clear edge over all other biometric systems which require a formidable amount of…
Nowadays, mobile smart devices are widely used in daily life. It is increasingly important to prevent malicious users from accessing private data, thus a secure and convenient authentication method is urgently needed. Compared with common…
Human identification plays an important role in human-computer interaction. There have been numerous methods proposed for human identification (e.g., face recognition, gait recognition, fingerprint identification, etc.). While these methods…
Gait abnormality detection is critical for the early discovery and progressive tracking of musculoskeletal and neurological disorders, such as Parkinson's and Cerebral Palsy. Especially, analyzing the foot-floor contacts during walking…
In this work, we investigate the use of backscattered mm-wave radio signals for the joint tracking and recognition of identities of humans as they move within indoor environments. We build a system that effectively works with multiple…
In intelligent building management, knowing the number of people and their location in a room are important for better control of its illumination, ventilation, and heating with reduced costs and improved comfort. This is typically achieved…
An understanding of pedestrian dynamics is indispensable for numerous urban applications including the design of transportation networks and planing for business development. Pedestrian counting often requires utilizing manual or technical…
This study proposes a personal identification technique that applies machine learning with a two-layered convolutional neural network to spectrogram images obtained from radar echoes of a target person in motion. The walking and sitting…
Tracking of occupants within buildings has become a topic of interest in the past decade. Occupant tracking has been used in the public safety, energy conservation, and marketing fields. Various methods have been demonstrated which can…
Person re-identification is critical in surveillance applications. Current approaches rely on appearance based features extracted from a single or multiple shots of the target and candidate matches. These approaches are at a disadvantage…
Gait recognition is the process of identifying humans from their bipedal locomotion such as walking or running. As such, gait data is privacy sensitive information and should be anonymized where possible. With the rise of higher quality…
Recent studies have shown how motion-based biometrics can be used as a form of user authentication and identification without requiring any human cooperation. This category of behavioural biometrics deals with the features we learn in our…
Continuous authentication utilizes automatic recognition of certain user features for seamless and passive authentication without requiring user attention. Such features can be divided into categories of physiological biometrics and…
Recently, \textit{passive behavioral biometrics} (e.g., gesture or footstep) have become promising complements to conventional user identification methods (e.g., face or fingerprint) under special situations, yet existing sensing…
We introduce a novel, accurate and practical system for real-time people tracking and identification. We used a Kinect V2 sensor for tracking that generates a body skeleton for up to six people in the view. We perform identification using…
We describe a novel metric-based learning approach that introduces a multimodal framework and uses deep audio and geophone encoders in siamese configuration to design an adaptable and lightweight supervised model. This framework eliminates…