Deep learning for image and point cloud fusion in autonomous driving: A review
Autonomous vehicles were experiencing rapid development in the past few years. However,
achieving full autonomy is not a trivial task, due to the nature of the complex and dynamic …
achieving full autonomy is not a trivial task, due to the nature of the complex and dynamic …
Multiple object tracking: A literature review
Abstract Multiple Object Tracking (MOT) has gained increasing attention due to its academic
and commercial potential. Although different approaches have been proposed to tackle this …
and commercial potential. Although different approaches have been proposed to tackle this …
[HTML][HTML] A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets
K Bayoudh, R Knani, F Hamdaoui, A Mtibaa - The Visual Computer, 2022 - Springer
The research progress in multimodal learning has grown rapidly over the last decade in
several areas, especially in computer vision. The growing potential of multimodal data …
several areas, especially in computer vision. The growing potential of multimodal data …
Tracking objects as points
Tracking has traditionally been the art of following interest points through space and time.
This changed with the rise of powerful deep networks. Nowadays, tracking is dominated by …
This changed with the rise of powerful deep networks. Nowadays, tracking is dominated by …
Deep multimodal fusion by channel exchanging
Deep multimodal fusion by using multiple sources of data for classification or regression has
exhibited a clear advantage over the unimodal counterpart on various applications. Yet …
exhibited a clear advantage over the unimodal counterpart on various applications. Yet …
3d multi-object tracking: A baseline and new evaluation metrics
3D multi-object tracking (MOT) is an essential component for many applications such as
autonomous driving and assistive robotics. Recent work on 3D MOT focuses on developing …
autonomous driving and assistive robotics. Recent work on 3D MOT focuses on developing …
Eagermot: 3d multi-object tracking via sensor fusion
Multi-object tracking (MOT) enables mobile robots to perform well-informed motion planning
and navigation by localizing surrounding objects in 3D space and time. Existing methods …
and navigation by localizing surrounding objects in 3D space and time. Existing methods …
Extendable multiple nodes recurrent tracking framework with RTU++
Recently, tracking-by-detection has become a popular paradigm in Multiple-object tracking
(MOT) for its concise pipeline. Many current works first associate the detections to form track …
(MOT) for its concise pipeline. Many current works first associate the detections to form track …
Gnn3dmot: Graph neural network for 3d multi-object tracking with 2d-3d multi-feature learning
Abstract 3D Multi-object tracking (MOT) is crucial to autonomous systems. Recent work uses
a standard tracking-by-detection pipeline, where feature extraction is first performed …
a standard tracking-by-detection pipeline, where feature extraction is first performed …
Box-aware feature enhancement for single object tracking on point clouds
Current 3D single object tracking approaches track the target based on a feature
comparison between the target template and the search area. However, due to the common …
comparison between the target template and the search area. However, due to the common …