Emerging Internet of Things (IoT) and mobile computing applications are expected to
support latency-sensitive deep neural network (DNN) workloads. To realize this vision, the …

Emerging Internet of Things (IoT) and mobile computing applications are expected to
support latency-sensitive deep neural network (DNN) workloads. To realize this vision, the …

Video analytics with Deep Neural Networks (DNNs) empowers many vision-based
applications. However, deploying DNN models for video analytics services must address the …

Recent advances in computer vision and neural networks have made it possible for more
surveillance videos to be automatically searched and analyzed by algorithms rather than …

Detection-driven real-time video analytics require continuous detection of objects contained
in the video frames using deep learning models like YOLOV3, EfficientDet, etc. However …

Mobile video analytics applications, such as smart driving, VR/AR, and video surveillance,
have become increasingly popular due to the proliferation of mobile devices. These …

Video object detection is a fundamental technology of intelligent video analytics for Internet
of Things (IoT) applications. However, even with extraordinary detection accuracy …

Currently, massive video inference tasks are processed through edge-cloud collaboration.
However, the diverse scenarios make it difficult to allocate the inference tasks efficiently …

In this paper, we present Mondrian, an edge system that enables high-performance object
detection on high-resolution video streams. Many lightweight models and system …

Deep learning–based video analytics demands high network bandwidth to ferry the large
volume of data when deployed on the cloud. When incorporated at the edge side, only …