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 …

While recent advances in deep neural networks (DNNs) enabled remarkable performance
on various computer vision tasks, it is challenging for edge devices to perform real-time …

Recently, edge computing has received considerable attention as a promising solution to
provide deep learning-based video analysis services in real-time. However, due to the …

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 …

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 …

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

Deep learning models have significantly improved object detection, which is essential for
visual sensing. However, their increasing complexity results in higher latency and resource …

In the last decade, deep neural network (DNN)-based object detection technologies have
received significant attention as a promising solution to implement a variety of image …

Object detection is considered one of the most challenging problems in this field of computer
vision, as it involves the combination of object classification and object localization within a …

The deep learning technique has recently led to significant improvement in object-detection
accuracy. Numerous object detection schemes have been designed to process each frame …