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Virtual Worlds as Proxy for Multi-Object Tracking Analysis
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| Adrien Gaidon, Qiao Wang, Yohann Cabon, Eleonora Vig |
| CVPR, Las Vegas, Nevada, USA; June 26 - July 1st, 2016. |
| 2015-085.pdf |
Assessing performance on data not seen during training is critical in order to validate machine learning models. In computer vision, however, experimentally measuring the actual robustness and generalization performance of high-level recognition methods is difficult in practice, especcially in video analyzis, due to high data acquisition and labeling costs.
Furthermore, it is sometimes nearly impossible to acquire data for some test scenarios of interest (e.g., storms, accidents, …). In this work, we show how to leverage the recent progress in computer graphics (especially off-the-shelf tools like game engines) to generate photo-realistic virtual worlds useful to assess the performance of video analysis algorithms.
The main benefits of our approach are (i) the low cost of data generation, including with high-quality detailed annotations, (ii) the flexibility to automatically generate rich and varied scenes and their annotations, including under rare conditions to perform “what-if” and “ceteris paribus” analysis, and (iii) techniques to quantify the “transferability of conclusions” from synthetic to real-world data.
The main novel idea behind our approache consists in initializing the virtual worlds from 3D synthetic clones of real-world video sequences.
Citation: CVPR 2016, Las Vegas, Nevada, USA; June 26th – July 1st, 2016.
Also: MIT Technology Review | 16th March 2016
Modern computer vision algorithms typically require expensive data acquisition and accurate manual labeling. In this work, we instead leverage the recent progress in computer graphics to generate fully labeled, dynamic, and photo-realistic proxy virtual worlds. We propose an efficient real-to-virtual world cloning method, and validate our approach by building and publicly releasing a new video dataset, called “Virtual KITTI”, automatically labeled with accurate ground truth for object detection, tracking, scene and instance segmentation, depth, and optical flow. We provide quantitative experimental evidence suggesting that (i) modern deep learning algorithms pre-trained on real data behave similarly in real and virtual worlds, and (ii) pre-training on virtual data improves performance. As the gap between real and virtual worlds is small, virtual worlds enable measuring the impact of various weather and imaging conditions on recognition performance, all other things being equal. We show these factors may affect drastically otherwise high-performing deep models for tracking.
To make robots autonomous in real-world everyday spaces, they should be able to learn from their interactions within these spaces, how to best execute tasks specified by non-expert users in a safe and reliable way. To do so requires sequential decision-making skills that combine machine learning, adaptive planning and control in uncertain environments as well as solving hard combinatorial optimisation problems. Our research combines expertise in reinforcement learning, computer vision, robotic control, sim2real transfer, large multimodal foundation models and neural combinatorial optimisation to build AI-based architectures and algorithms to improve robot autonomy and robustness when completing everyday complex tasks in constantly changing environments.
VISION
The research we conduct on expressive visual representations is applicable to visual search, object detection, image classification and the automatic extraction of 3D human poses and shapes that can be used for human behavior understanding and prediction, human-robot interaction or even avatar animation. We also extract 3D information from images that can be used for intelligent robot navigation, augmented reality and the 3D reconstruction of objects, buildings or even entire cities.
Our work covers the spectrum from unsupervised to supervised approaches, and from very deep architectures to very compact ones. We’re excited about the promise of big data to bring big performance gains to our algorithms but also passionate about the challenge of working in data-scarce and low-power scenarios.
Furthermore, we believe that a modern computer vision system needs to be able to continuously adapt itself to its environment and to improve itself via lifelong learning. Our driving goal is to use our research to deliver embodied intelligence to our users in robotics, autonomous driving, via phone cameras and any other visual means to reach people wherever they may be.
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