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 …

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 …

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

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 …

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 targets to simultaneously localize the bounding boxes of the objects
and identify their classes in a given video. One challenge for video object detection is to …

Detecting tiny objects in a high-resolution video is challenging because the visual
information is little and unreliable. Specifically, the challenge includes very low resolution of …

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 …

Video object detection is a fundamental research task for scene understanding. Compared
with object detection in images, object detection in videos has been less researched due to …

Occlusion is one of the most significant challenges encountered by object detectors and
trackers. While both object detection and tracking has received a lot of attention in the past …