Numerous edge applications that rely on video analytics demand precise, low-latency
processing of multiple video streams from cameras. When these cameras are mobile, such …

Many mobile applications have been developed to apply deep learning for video analytics.
Although these advanced deep learning models can provide us with better results, they also …

Edge computing is a highly promising technology that empowers mobile devices to offload
video streaming tasks to edge servers, thereby improving the video stream analysis …

Abstract The emergence of Mobile Edge Computing (MEC) alleviates the large transmission
latency resulting from the traditional cloud computing. For the compute-intensive requests …

Applying deep neural networks (DNNs) to video analytics tasks has drawn attention from
both academic and industry communities. However, due to the high computational …

Multi-camera systems are now widely employed across numerous domains. The
exponential growth of deep learning has simplified the implementation of advanced video …

The fifth-generation mobile network is characterized as the edge of wireless connectivity for
all intelligent automation. Technically, the services' requirements for Quality of Service (QoS) …

Supporting Edge AI services is one of the most exciting features of future mobile networks.
These services involve the collection and processing of voluminous data streams, right at …

The efficient processing of video streams is a key component in many emerging Internet of
Things (IoT) and edge applications, such as Virtual and Augmented Reality (V/AR) and self …

Efficient video processing is a critical component in many IoMT applications to detect events
of interest. Presently, many window optimization techniques have been proposed in event …