Real-time video analytics on the edge is challenging as the computationally constrained
resources typically cannot analyse video streams at full fidelity and frame rate, which results …

A significant amount of redundancy exists between consecutive frames of a video. Object
detectors typically produce detections for one image at a time, without any capabilities for …

Video data is the biggest IoT data which is challenging for effective analysis with good
performance. Object misdetection is usually inevitable in edge-based distributed cross …

Over the last several years it has been shown that image-based object detectors are
sensitive to the training data and often fail to generalize to examples that fall outside the …

Single-frame object detectors perform well on videos sometimes, even without temporal
context. However, challenges such as occlusion, motion blur, and rare poses of objects are …

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 …

Real-time intelligent video processing on embedded devices with low power consumption
can be useful for applications like drone surveillance, smart cars, and more. However, the …

Recent advances in single-frame object detection and segmentation techniques have
motivated a wide range of works to extend these methods to process video streams. In this …

Recent cutting-edge feature aggregation paradigms for video object detection rely on
inferring feature correspondence. The feature correspondence estimation problem is …

Vehicle detection in videos is a valuable but challenging technology in traffic monitoring.
Due to the advantage of real-time detection, Single Shot MultiBox Detector (SSD) is often …