The use of video streaming has been growing rapidly in the recent years and has beenutilized in various applications. Many of these applications are latency sensitive, butthe latency requirements varies largely from one application to another. The...
The use of video streaming has been growing rapidly in the recent years and has beenutilized in various applications. Many of these applications are latency sensitive, butthe latency requirements varies largely from one application to another. The...
Recent advances in Deep Learning have made possible distributed multi-camera IoT vision analytics targeted at a variety of surveillance applications involving automated real-time analysis of events from multiple video perspectives. However, the la...
AI processing has been a big area of focus for the research community for quite some years. The growing computation capability of edge devices has allowed Computer Vision to make use of AI techniques with greater efficiency and throughput. This ap...
The prevalence of computation-intensive and latency-sensitive mobile applications, such as mobile augmented reality (MAR) and autonomous driving, has an utmost effect on resource-limited mobile clients. Mobile edge computing (MEC) is proposed to b...
The proliferation of connected devices creates various use cases and heterogeneous services, e.g., augmented/virtual reality (AR/VR), vehicle-to-everything (V2X), and mobile artificial intelligence.These services and use cases have diverse network...
The Edge computing paradigm seeks to bring Cloud-like compute capabilities close to the Edge of the network, next to where the data is generated, so as to minimize the data communication latency. Edge applications such as autonomous driving, surve...
This article presents two methods, REVAMPT and CARPe Posterum. REVAMPT, or Real-time Edge Video Analytics for Multi-person Privacy-aware Tracking, is an integrated system for privacy-built-in pedestrian re-identification. REVAMPT presents novel al...