Advanced video analytic systems, including scene classification and object detection, have
seen widespread success in various domains such as smart cities and autonomous …

Abstract Deep Neural Networks (DNNs) have shown exceptional promise in providing
Artificial Intelligence (AI) to many computer vision applications. Nevertheless, complex …

Deep neural networks (DNNs) have been widely used in many intelligent applications such
as object recognition and automatic driving due to their superior performance in conducting …

Edge computing has gained momentum in recent years, and can provide more immediate
analysis of streaming video data. However, the edge devices often lack the computing …

Deep Neural Networks (DNNs) are widely used to analyze the abundance of data collected
by massive Internet-of-Thing (IoT) devices. The traditional approaches usually send the data …

Advances in federated learning and edge computing advocate for deep learning models to
run at edge devices for video analysis. However, the captured video frame rate is too high to …

This paper proposes a method that jointly learns to detect objects at the current frame and
forecast the next frame's future feature map. Previous offline detectors have shown the …

Deep neural networks (DNN) have become indispensable tools for intelligent applications
today. The demand for deploying DNN on the edge devices increases dramatically …

In recent years, machine learning research has largely shifted focus from the cloud to the
edge. While the resulting algorithm-and hardware-level optimizations have enabled local …

In recent years, real-time video processing has advanced thanks to dramatic improvements
in object detection algorithms. This has increased the demand for video object detection by …