Radar is usually more robust than the camera in severe driving scenarios, eg, weak/strong
lighting and bad weather. However, unlike RGB images captured by a camera, the semantic …

In this paper, we propose a novel framework for multi-target multi-camera tracking (MTMCT)
of vehicles based on metadata-aided re-identification (MA-ReID) and the trajectory-based …

Multi-target multi-camera tracking (MTMCT), ie, tracking multiple targets across multiple
cameras, is a crucial technique for smart city applications. In this paper, we propose an …

Current multi-object tracking and segmentation (MOTS) methods follow the tracking-by-
detection paradigm and adopt 2D or 3D convolutions to extract instance embeddings for …

3D point cloud maps are widely used in robotic tasks like localization and planning.
However, dynamic objects, such as cars and pedestrians, can introduce ghost artifacts …

While machine learning (ML) provides a great tool for image analysis, obtaining accurate
fracture segmentation from high-resolution core images is challenging. A major reason is …

Abstract Multi-Target Multi-Camera Tracking (MTMCT) of vehicles is a challenging task in
smart city related applications. The main challenge of MTMCT is how to accurately match the …

In IoT systems, the goal of multiview detection for multiple visual sensor networks is to use
multiple camera perspectives to address occlusion challenges with multiview aggregation …

We propose a method for multi-object tracking and segmentation based on a novel memory-
based mechanism to associate tracklets. The proposed tracker, MeNToS, addresses …

Multi-camera Multiple People Tracking (MMPT) is a challenging task in advanced visual
monitoring systems. The main challenge of MMPT is how to accurately match the single …