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In spite of the increasing concerns raised by privacy advocates against the intrusive deployment of large scale surveillance cameras, the research community has progressed with a remarkable pace into the area of smart visual surveillance. The automation for surveillance systems becomes an obligation to avoid human errors and ensure an efficient str...
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Social distancing is crucial to restrain the spread of diseases such as COVID-19, but complete adherence to safety guidelines is not guaranteed. Monitoring social distancing through mass surveillance is paramount to develop appropriate mitigation plans and exit strategies. Nevertheless, it is a labor-intensive task that is prone to human error and...
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... Por un lado, el enfoque basado en modelos se sumerge en características vinculadas al movimiento del cuerpo humano, como la flexión de extremidades y la frecuencia de pasos (Bouchrika, I., 2018). Por otro lado, el enfoque basado en apariencias se embarca en la tarea de extraer la esencia de la forma de caminar de una persona a través de su silueta (Bouchrika, I., 2018). ...
En un contexto donde el reconocimiento de personas cobra protagonismo, particularmente en el ámbito de la seguridad, emerge el Reconocimiento del Andar Humano (rah) como una técnica biométrica clave. Este enfoque, centrado en la forma de caminar, ha experimentado un notable auge en la investigación reciente, gracias a sus ventajas intrínsecas. La capacidad de llevar a cabo el reconocimiento a distancia, incluso sin el consentimiento explícito, posiciona al rah como una herramienta de vanguardia. Se distinguen dos enfoques computacionales: el basado en modelos, que explora el movimiento del cuerpo humano, y el basado en apariencias, que extrae la esencia de la forma de caminar desde la silueta. La versatilidad del rah radica en su independencia respecto al tipo de cámara utilizada, proporcionando información detallada sobre ángulos de flexión, frecuencia de zancada y longitud de partes del cuerpo. Este trabajo ofrece un análisis evolutivo del rah a lo largo del tiempo, destacando contribuciones significativas que han marcado pautas en la investigación.
... Biometric recognition systems automatically verify or determine persons' identities in input images and/or videos using human biometric traits. Nowadays, the concept of "biometrics at a distance" or "remote biometrics" is gaining increasing importance to provide robust methods for re-identification, intelligent monitoring, and intelligent surveillance [5,6,7] with the added value that they do not require the users' collaboration. The adoption of CCTV camera networks for security is a typical example of sophisticated computer vision at a distance using distributed sensor networks [8]. ...
... In both [12], [13] provided a comprehensive survey that covered general topics in regards to gait recognition. Meanwhile, a study published by [14] examined the recognition of gaits based on vision, while proposed a similar concept for smart visual surveillance [15]. Furthermore, a summary of the work with regard to the gait-based re-identification of individuals was published by [16] in 2019. ...
Human gait recognition is a biometric technique that has been utilized for security purposes for the last decade. Gait recognition is an appealing biometric modality that aims to identify individuals based on the way they walk. The outbreak of the novel coronavirus (COVID-19), has spread across the world. The number of people infected with COVID-19 is rising rapidly throughout the world. Even though some vaccines for this pandemic have been developed to minimize the effects of COVID-19, deep learning-based gait recognition techniques have shown themselves to be an effective tool for identifying the individuals wearing face mask in COVID-19 pandemic. These techniques play an important part in reducing the rate of COVID-19 spreading throughout the world in the context of the COVID-19 pandemic. Deep learning methods are currently dominating the state-of-the-art in gait recognition and have fostered real-world applications. The main objective of this paper is to provide a comprehensive overview of recent advancements in gait recognition with deep learning, including datasets, test protocols, state-of-the-art solutions, challenges, and future research directions. The purpose of this discussion is to identify current challenges that need to be addressed as well as to suggest some directions for future research that could be explored.
... This is a specific identification framework for implementing a biometrics system capable of handling fingerprints, iris scans, and other data types for integrating the power of biometrics to provide a safe identity verification solution for voters. On the other hand, biometrics address the prevalent identity problem, which is now a global dilemma [9], [10]. In this paper, a multimodal biometric is recommended to hack up with all available technology and voter's needs. ...
This paper aims at investigating the feasibility of adopting a secure e-voting based on the authenticity of biometrics in conducting the election of Jordan parliamentarians consol. It firstly investigates the Jordanian citizens’ acceptance level of an e-voting system based on biometric credentials as a supportive solution to their attitude, intention, and trust in actual participation. The successor phases are directed to validate the feasibility of adopting a biometric-based e-voting system by involving the Delphi method of three rounds for collecting and analysing data. The initial use case diagram, interview questions, and resultant queries are all mapped to construct the proposed conceptual framework. The results of multi phases methodology allow for development and recommend a conceptual framework for implementing an e-voting system with all acting schemas that represent the different stages of the election process. Moreover, the proposed conceptual framework was developed with thematic regulations that align with the experts' consensuses and the current Jordan parliament election law.
... The standard of evidence admissibility in the United States also has been discussed [104,105] Furthermore, there are many papers that discuss the reliability of gait analysis [60,68,72,89,93,94,96,105,106,108,116,120,123,125,126,129,130,132,136,140,144]. ...
... Identifying suspicious behaviors can thus be carried out effectively (Anderez et al., 2021). Smart Visual Surveillance applications can be implemented using gait analysis, including re-identification and forensic analysis, since gaits can be captured at a distance without the awareness or cooperation of the subjects (Bouchrika, 2018). ...
Emotion recognition is useful in many applications such as preventing crime or improving customer satisfaction. Most of current methods are performed using facial features, which require close-up face information. Such information is difficult to capture with normal security cameras. The advantage of using gait and posture over conventional biometrics such as facial features is that gaits and postures can be obtained unobtrusively from faraway, even in a noisy environment. This study aims to investigate and analyze the relationship between human emotions and their gaits or postures. We collected a dataset made from the input of 49 participants for our experiments. Subjects were instructed to walk naturally in a circular walking path, while watching emotion-inducing videos on Microsoft HoloLens 2 smart glasses. An OptiTrack motion-capturing system was used for recording the gaits and postures of participants. The angles between body parts and walking straightness were calculated as features for comparison of body-part movements while walking under different emotions. Results of statistical analyses show that the subjects' arm swings are significantly different among emotions. And the arm swings on one side of the body could reveal subjects' emotions more obviously than those on the other side. Our results suggest that the arm movements together with information of arm side and walking straightness can reveal the subjects' current emotions while walking. That is, emotions of humans are unconsciously expressed by their arm swings, especially by the left arm, when they are walking in a non-straight walking path. We found that arm swings in happy emotion are larger than arm swings in sad emotion. To the best of our knowledge, this study is the first to perform emotion induction by showing emotion-inducing videos to the participants using smart glasses during walking instead of showing videos before walking. This induction method is expected to be more consistent and more realistic than conventional methods. Our study will be useful for implementation of emotion recognition applications in real-world scenarios, since our emotion induction method and the walking direction we used are designed to mimic the real-time emotions of humans as they walk in a non-straight walking direction.
... Another good example is about surveillance and security application, which is related to intelligent video surveillance topic of this book. A survey conducted by [3] reveals that gait analysis is very useful for crime prevention applications. In recent years, a CCTV camera system is now a standard equipment, which is installed in almost every public places. ...
Emotion recognition is an attractive research field because of its usefulness. Most methods for detecting and analyzing emotions depend on facial features so the close-up facial information is required. Unfortunately, high-resolution facial information is difficult to be captured from a standard security camera. Unlike facial features, gaits and postures can be obtained noninvasively from a distance. We proposed a method to collect emotional gait data with real-time emotion induction. Two gait datasets consisting of total 72 participants were collected. Each participant walked in circular pattern while watching emotion induction videos shown on Microsoft HoloLens 2 smart glasses. OptiTrack motion capturing system was used to capture the participants\' gaits and postures. Effectiveness of emotion induction was evaluated using self-reported emotion questionnaire. In our second dataset, additional information of each subject such as dominant hand, dominant foot, and dominant brain side was also collected. These data can be used for further analyses. To the best of our knowledge, emotion induction method shows the videos to subjects while walking has never been used in other studies. Our proposed method and dataset have the potential to advance the research field about emotional recognition and analysis, which can be used in real-world applications.
... Different from the prevailing human identification methods, gait is a unique biomarker that can be identified at a distance without human cooperation. The advantages of gait recognition in remote monitoring [5] make it essential in crime prevention, forensic identification and social security. ...
As an important biomarker for human identification, human gait can be collected at a distance by passive sensors without subject cooperation, which plays an essential role in crime prevention, security detection and other human identification applications. At present, most research works are based on cameras and computer vision techniques to perform gait recognition. However, vision-based methods are not reliable when confronting poor illuminations, leading to degrading performances. In this paper, we propose a novel multimodal gait recognition method, namely GaitFi, which leverages WiFi signals and videos for human identification. In GaitFi, Channel State Information (CSI) that reflects the multi-path propagation of WiFi is collected to capture human gaits, while videos are captured by cameras. To learn robust gait information, we propose a Lightweight Residual Convolution Network (LRCN) as the backbone network, and further propose the two-stream GaitFi by integrating WiFi and vision features for the gait retrieval task. The GaitFi is trained by the triplet loss and classification loss on different levels of features. Extensive experiments are conducted in the real world, which demonstrates that the GaitFi outperforms state-of-the-art gait recognition methods based on single WiFi or camera, achieving 94.2% for human identification tasks of 12 subjects.
... While face, eyes, and fingerprint biometrics are morphed with face masks, oversized glasses, and gloves, gait biometric-based person identification is the best option for visual surveillance in this scenario. Gait recognition for visual surveillance includes age group estimation [1][2][3], ethnicity classification [4], biometric identification [5][6][7], gender recognition [8][9][10], and suspect identification in forensics [11,12]. Moreover, gait biometrics is hard to spoof and impossible to hide [13][14][15]. ...
This study aimed to develop a vision-based gait recognition system for person identification. Gait is the soft biometric trait recognizable from low-resolution surveillance videos, where the face and other hard biometrics are not even extractable. The gait is a cycle pattern of human body locomotion that consists of two sequential phases: swing and stance. The gait features of the complete gait cycle, referred to as gait signature, can be used for person identification. The proposed work utilizes gait dynamics for gait feature extraction. For this purpose, the spatio temporal power spectral gait features are utilized for gait dynamics captured through sub-pixel motion estimation, and they are less affected by the subject’s appearance. The spatio temporal power spectral gait features are utilized for a quadratic support vector machine classifier for gait recognition aiming for person identification. Spatio temporal power spectral preserves the spatiotemporal gait features and is adaptable for a quadratic support vector machine classifier-based gait recognition across different views and appearances. We have evaluated the gait features and support vector machine classifier-based gait recognition on a locally collected gait dataset that captures the effect of view variance in high scene depth videos. The proposed gait recognition technique achieves significant accuracy across all appearances and views.
... The recognition and identification tasks developed by video surveillance systems tend to be mainly performed upon people and vehicles. To identify a person, there are different biometric characteristics that are individual and impossible to replicate, such as the face, voice, eyes or even the arrangement of blood vessels [14,15] (in vehicles, it is normal to resort to license plates). ...
Security cameras have been proven to be particularly useful in preventing and combating crime through identification tasks. Here, two areas can be mainly distinguished: person and vehicle identification. Automatic license plate readers are the most widely used tool for vehicle identification. Although these systems are very effective, they are not reliable enough in certain circumstances. For example, due to traffic jams, vehicle position or weather conditions, the sensors cannot capture an image of the entire license plate. However, there is still a lot of additional information in the image which may also be of interest, and that needs to be analysed quickly and accurately. The correct use of the processing mechanisms can significantly reduce analysis time, increasing the efficiency of video cameras significantly. To solve this problem, we have designed a solution based on two technologies: license plate recognition and vehicle re-identification. For its development and testing, we have also created several datasets recreating a real environment. In addition, during this article, it is also possible to read about some of the main artificial intelligence techniques for these technologies, as they have served as the starting point for this research.
