Over the last 10 years, huge advances have been made in the areas of sensor technologies
and processing platforms, pushing forward developments in the field of autonomous …

In the recent literature, on the one hand, many 3D multi-object tracking (MOT) works have
focused on tracking accuracy and neglected computation speed, commonly by designing …

This article proposes a multi-object tracking (MOT) method called StrongFusionMOT, which
fuses the information of light detection and ranging (LiDAR) and camera sensors. The major …

Computation and memory resource management strategies are the backbone of continuous
object tracking in intelligent vehicle orchestration. Multi-object tracking generates enormous …

Real-time object detection and ranging of multiple objects on the road are the essential
tasks in the field of autonomous driving. In this paper, we introduce a system for …

Artificial Intelligence is being adapted by the world since past few years and deep learning
played a crucial role in it. This paper focuses on deep learning and how it is applied to …

Actively exploring unknown indoor environments using an RGB-D camera is a challenging
task, especially when utilizing the feature-based visual simultaneous localization and …

Event-based vision is an emerging field of computer vision that offers unique properties,
such as asynchronous visual output, high temporal resolutions, and dependence on …

Object tracking is one of the most important problems in computer vision applications such
as robotics, autonomous driving, and pedestrian movement. There has been a significant …

Multi-object tracking (MOT) systems typically rely on object detection results for tracking, so
the accuracy of the MOT system is significantly affected by the error of the detector. Changes …