Data association algorithm for large-scale multi-object tracking with complex interactions

2021 ◽  
Vol 30 (06) ◽  
Author(s):  
Garret Vo ◽  
Dmitri Zakharov ◽  
Chiwoo Park
Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2894
Author(s):  
Minh-Quan Dao ◽  
Vincent Frémont

Multi-Object Tracking (MOT) is an integral part of any autonomous driving pipelines because it produces trajectories of other moving objects in the scene and predicts their future motion. Thanks to the recent advances in 3D object detection enabled by deep learning, track-by-detection has become the dominant paradigm in 3D MOT. In this paradigm, a MOT system is essentially made of an object detector and a data association algorithm which establishes track-to-detection correspondence. While 3D object detection has been actively researched, association algorithms for 3D MOT has settled at bipartite matching formulated as a Linear Assignment Problem (LAP) and solved by the Hungarian algorithm. In this paper, we adapt a two-stage data association method which was successfully applied to image-based tracking to the 3D setting, thus providing an alternative for data association for 3D MOT. Our method outperforms the baseline using one-stage bipartite matching for data association by achieving 0.587 Average Multi-Object Tracking Accuracy (AMOTA) in NuScenes validation set and 0.365 AMOTA (at level 2) in Waymo test set.


2021 ◽  
Author(s):  
Jessica Fayne ◽  
Huilin Huang ◽  
Mike Fischella ◽  
Yufei Liu ◽  
Zhaoxin Ban ◽  
...  

<p>Extreme precipitation, a critical factor in flooding, has selectively increased with warmer temperatures in the Western U.S. Despite this, the streamflow measurements have captured no noticeable increase in large-scale flood frequency or intensity. As flood studies have mostly focused on specific flood events in particular areas, analyses of large-scale floods and their changes have been scarce. For floods during 1960-2013, we identify six flood generating mechanisms (FGMs) that are prominent across the Western U.S., including atmospheric rivers and non-atmospheric rivers, monsoons, convective storms, radiation-driven snowmelt, and rain-on-snow, in order to identify to what extent different types of floods are changing based on the dominant FGM. The inconsistency between extreme precipitation and lack of flood increase suggests that the impact of climate change on flood risk has been modulated by hydro-meteorological and physiographic processes such as sharp increases in temperature that drive increased evapotranspiration and decreased soil moisture. Our results emphasize the importance of FGMs in understanding the complex interactions of flooding and climatic changes and explain the broad spatiotemporal changes that have occurred across the vast Western U.S. for the past 50 years.</p>


Author(s):  
Matthias Müller ◽  
Adel Bibi ◽  
Silvio Giancola ◽  
Salman Alsubaihi ◽  
Bernard Ghanem

2007 ◽  
Vol 292 (2) ◽  
pp. L367-L377 ◽  
Author(s):  
Joost B. Vos ◽  
Nicole A. Datson ◽  
Klaus F. Rabe ◽  
Pieter S. Hiemstra

The epithelial surface of the airways is the largest barrier-forming interface between the human body and the outside world. It is now well recognized that, at this strategic position, airway epithelial cells play an eminent role in host defense by recognizing and responding to microbial exposure. Conversely, inhaled microorganisms also respond to contact with epithelial cells. Our understanding of this cross talk is limited, requiring sophisticated experimental approaches to analyze these complex interactions. High-throughput technologies, such as DNA microarray analysis and serial analysis of gene expression (SAGE), have been developed to screen for gene expression levels at large scale within single experiments. Since their introduction, these hypothesis-generating technologies have been widely used in diverse areas such as oncology and brain research. Successful application of these genomics-based technologies has also revealed novel insights in host-pathogen interactions in both the host and pathogen. This review aims to provide an overview of the SAGE and microarray technology illustrated by their application in the analysis of host-pathogen interactions. In particular, the interactions between epithelial cells in the human lungs and clinically relevant microorganisms are the central focus of this review.


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