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 …

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 …

Mobile devices such as drones and autonomous vehicles increasingly rely on object
detection (OD) through deep neural networks (DNNs) to perform critical tasks such as …

Convolutional neural networks have been successfully applied for video content detection in
the last years. However, such cognitive models usually demand the availability of several …

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

Developing object detection and tracking on resource-constrained embedded systems is
challenging. While object detection is one of the most compute-intensive tasks from the …

We propose a novel approach to boost the performance of generic object detectors on
videos by learning videospecific features using a deep neural network. The insight behind …

Deep Neural Network (DNN) trained object detec-tors are widely deployed in many mission-
critical systems for real time video analytics at the edge, such as autonomous driving, video …

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 …

Detection&tracking framework enables real-time object detection services on resource-
limited mobile devices in edge networks, where mobile devices only offload few key frames …