Video object detection, a basic task in the computer vision field, is rapidly evolving and
widely used. In recent years, deep learning methods have rapidly become widespread in the …

Although there are well established object detection methods based on static images, their
application to video data on a frame by frame basis faces two shortcomings:(i) lack of …

Single frame data contains finite information which limits the performance of the existing
vision-based multi-camera 3D object detection paradigms. For fundamentally pushing the …

Deep learning models are efficient in learning the features that assist in understanding
complex patterns precisely. This study proposed a computerized process of classifying skin …

Owing to effective and flexible data acquisition, unmanned aerial vehicles (UAVs) have
recently become a hotspot across the fields of computer vision (CV) and remote sensing …

Detection Transformer (DETR) and Deformable DETR have been proposed to eliminate the
need for many hand-designed components in object detection while demonstrating good …

The ResNet and its variants have achieved remarkable successes in various computer
vision tasks. Despite its success in making gradient flow through building blocks, the …

Object detection is a fundamental building block of video analytics applications. While
Neural Networks (NNs)-based object detection models have shown excellent accuracy on …

Tracking humans in crowded video sequences is an important constituent of visual scene
understanding. Increasing crowd density challenges visibility of humans, limiting the …

Video Visual Relation Detection (VidVRD) aims to semantically describe the dynamic
interactions across visual concepts localized in a video in the form of subject, predicate …