Person Identification with Visual Summary for a Safe Access to a Smart Home
SafeAccess is an integrated system designed to provide easier and safer access to a smart home for people with or without disabilities. The system is designed to enhance safety and promote the independence of people with disability (i.e., visually impaired). The key functionality of the system includes the detection and identification of human and generating contextual visual summary from the real-time video streams obtained from the cameras placed in strategic locations around the house. In addition, the system classifies human into groups (i.e. friends/families/caregiver versus intruders/burglars/unknown). These features allow the user to grant/deny remote access to the premises or ability to call emergency services. In this paper, we focus on designing a prototype system for the smart home and building a robust recognition engine that meets the system criteria and addresses speed, accuracy, deployment and environmental challenges under a wide variety of practical and real-life situations. To interact with the system, we implemented a dialog enabled interface to create a personalized profile using face images or video of friend/families/caregiver. To improve computational efficiency, we apply change detection to filter out frames and use Faster-RCNN to detect the human presence and extract faces using Multitask Cascaded Convolutional Networks (MTCNN). Subsequently, we apply LBP/FaceNet to identify a person and groups by matching extracted faces with the profile. SafeAccess sends a visual summary to the users with an MMS containing a person's name if any match found or as "Unknown", scene image, facial description, and contextual information. SafeAccess identifies friends/families/caregiver versus intruders/unknown with an average F-score 0.97 and generates a visual summary from 10 classes with an average accuracy of 98.01%.
Code (0)
등록된 구현이 없습니다.
Tasks
Change DetectionComputational EfficiencyPerson IdentificationSimilar Papers 제목 키워드 기반
Yes, this is what I was looking for! Towards Multi-modal Medical Consultation Concern Summary Generation
Over the past few years, the use of the Internet for healthcare-related tasks has grown by leaps and bounds, posing a challenge in effectively managing and processing information to ensure its efficient utilization. Duri…
Intent RecognitionPersonaMatrix: A Recipe for Persona-Aware Evaluation of Legal Summarization
Legal documents are often long, dense, and difficult to comprehend, not only for laypeople but also for legal experts. While automated document summarization has great potential to improve access to legal knowledge, prev…
Document SummarizationFrom Attributes to Natural Language: A Survey and Foresight on Text-based Person Re-identification
Text-based person re-identification (Re-ID) is a challenging topic in the field of complex multimodal analysis, its ultimate aim is to recognize specific pedestrians by scrutinizing attributes/natural language descriptio…
An Improved Deep Learning Architecture for Person Re-Identification
In this work we propose a method for simultaneously learning features and a corresponding similarity metric for person re-identification. We present a deep convolution architecture with layers specially designed to addre…
Deep LearningPerson Re-IdentificationSmall Data Image ClassificationAttribute De-biased Vision Transformer (AD-ViT) for Long-Term Person Re-identification
Person re-identification (re-ID) aims to retrieve images of the same identity from a gallery of person images across cameras and viewpoints. However, most works in person re-ID assume a short-term setting characterized b…
AttributePerson IdentificationPerson RecognitionPerson Re-Identification+1