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Track to Detect and Segment: An Online Multi-Object Tracker
Most online multi-object trackers perform object detection stand-alone i...
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Uncertainty Estimation in One-Stage Object Detection
Environment perception is the task for intelligent vehicles on which all...
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Simultaneous Object Detection, Tracking, and Event Recognition
The common internal structure and algorithmic organization of object det...
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Global Correlation Network: End-to-End Joint Multi-Object Detection and Tracking
Multi-object tracking (MOT) has made great progress in recent years, but...
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3D Object Detection and Tracking Based on Streaming Data
Recent approaches for 3D object detection have made tremendous progresse...
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Improving Generative Imagination in Object-Centric World Models
The remarkable recent advances in object-centric generative world models...
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How to Train Your Energy-Based Model for Regression
Energy-based models (EBMs) have become increasingly popular within compu...
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Uncertainty-Aware Voxel based 3D Object Detection and Tracking with von-Mises Loss
Object detection and tracking is a key task in autonomy. Specifically, 3D object detection and tracking have been an emerging hot topic recently. Although various methods have been proposed for object detection, uncertainty in the 3D detection and tracking tasks has been less explored. Uncertainty helps us tackle the error in the perception system and improve robustness. In this paper, we propose a method for improving target tracking performance by adding uncertainty regression to the SECOND detector, which is one of the most representative algorithms of 3D object detection. Our method estimates positional and dimensional uncertainties with Gaussian Negative Log-Likelihood (NLL) Loss for estimation and introduces von-Mises NLL Loss for angular uncertainty estimation. We fed the uncertainty output into a classical object tracking framework and proved that our method increased the tracking performance compared against the vanilla tracker with constant covariance assumption.
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