Virtual Worlds as Proxy for Multi-Object Tracking Analysis

Virtual Worlds as Proxy for Multi-Object Tracking Analysis

| Adrien Gaidon1*, Qiao Wang2*, Yohann Cabon1 Eleonora Vig1†
This paper introduces a novel approach to generating fully labeled, dynamic, and photo-realistic virtual worlds for multi-object tracking (MOT) analysis. The authors leverage advancements in computer graphics to create these virtual worlds, which are then used to build the "Virtual KITTI" dataset. This dataset includes 35 synthetic videos, each with ground truth annotations for object detection, tracking, depth, optical flow, and scene/instance segmentation. The paper presents a method for efficiently cloning real-world video sequences into virtual worlds and discusses the creation of the Virtual KITTI dataset. The authors validate the effectiveness of their approach by demonstrating that modern deep learning algorithms trained on real data perform similarly in both real and virtual worlds, and that pre-training on virtual data improves performance. They also show that variations in weather and imaging conditions can significantly impact the performance of high-performing models trained on real-world datasets, highlighting the need for more diverse training sets and unsupervised domain adaptation techniques. The paper concludes with a discussion on future directions, including expanding the Virtual KITTI dataset and exploring domain adaptation methods for more robust video understanding tasks.This paper introduces a novel approach to generating fully labeled, dynamic, and photo-realistic virtual worlds for multi-object tracking (MOT) analysis. The authors leverage advancements in computer graphics to create these virtual worlds, which are then used to build the "Virtual KITTI" dataset. This dataset includes 35 synthetic videos, each with ground truth annotations for object detection, tracking, depth, optical flow, and scene/instance segmentation. The paper presents a method for efficiently cloning real-world video sequences into virtual worlds and discusses the creation of the Virtual KITTI dataset. The authors validate the effectiveness of their approach by demonstrating that modern deep learning algorithms trained on real data perform similarly in both real and virtual worlds, and that pre-training on virtual data improves performance. They also show that variations in weather and imaging conditions can significantly impact the performance of high-performing models trained on real-world datasets, highlighting the need for more diverse training sets and unsupervised domain adaptation techniques. The paper concludes with a discussion on future directions, including expanding the Virtual KITTI dataset and exploring domain adaptation methods for more robust video understanding tasks.
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