The detection of objects via neural networks plays a key role in various video analytics, but
consumes huge resources. Due to the limited on-device computing capability, such real-time …

The detection of objects via neural networks plays a key role in various video analytics, but
consumes huge resources. Due to the limited computing capability at edges, such real-time …

Real-time on-device object detection for video analytics fails to meet the accuracy
requirement due to limited resources of mobile devices while offloading object detection …

Object detection is crucial in video analytics pipelines, but there is a need to optimize deep
neural networks (DNNs)-based object detection for resource-constrained Internet of Things …

This paper presents AdaVP, a continuous and real-time video processing system for mobile
devices without offloading. AdaVP uses Deep Neural Network (DNN) based tools like …

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

To deal with the challenges in video object detection (VOD), such as occlusion and motion
blur, many state-of-the-art video object detectors adopt a feature aggregation module to …

We present FlexPatch, a novel mobile system to enable accurate and real-time object
detection over high-resolution video streams. A widely-used approach for real-time video …

Recent approaches have achieved great success on still image object detection. Despite the
high accuracy, directly applying image object detectors for video object detection is rather …

Reliable object detection is essential for smart surveillance systems, and video surveillance
technology has been significantly improved thanks to Deep-Learning related techniques …