Advances in federated learning and edge computing advocate for deep learning models to
run at edge devices for video analysis. However, the captured video frame rate is too high to …

Object detection is one of the most important tasks of computer vision. It is usually performed
by evaluating a subset of the possible locations of an image, that are more likely to contain …

Deep convolutional and recurrent neural networks have delivered significant advancements
in object detection and tracking. However, current models handle detection and tracking …

With the growth of the Internet of Things, we see an increase in the importance of analysis of
data from the edge, often with the results needed in real-time. Indy Car series is one of the …

We design a fast car detection and tracking algorithm for traffic monitoring fisheye video
mounted on crossroads. We use ICIP 2020 VIP Cup dataset and adopt YOLOv5 as the …

Recent works have shown that combining object detection and tracking tasks, in the case of
video data, results in higher performance for both tasks, but they require a high frame-rate as …

Recent advancements in convolutional neural networks based object detection have
enabled analyzing the mounting video data with high accuracy. However, inference speed is …

Object detection algorithms, from high-resolution optical remote sensing images, have been
booming from the last few years. However, object tracking for high-resolution optical remote …

Internet of Things (IoT) has brought changes in many fields by joining physical space with
the cyber space. The IoT devices are becoming increasingly complex. With the rapid …

In the past decade, Deep Neural Networks (DNNs), eg, Convolutional Neural Networks,
achieved human-level performance in vision tasks such as object classification and …