Virtual Worlds as Proxy for Multi-Object Tracking Analysis (1605.06457v1)

Abstract: 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 (see http://www.xrce.xerox.com/Research-Development/Computer-Vision/Proxy-Virtual-Worlds), 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.

• The paper introduces a novel method to generate synthetic video datasets by cloning real-world sequences into photo-realistic virtual environments.
• The paper demonstrates effective transferability of multi-object tracking performance with MOTA discrepancies of less than 0.5% between virtual and real data.
• The paper shows that virtual pre-training improves tracker robustness under challenging conditions, emphasizing the need for advanced domain adaptation techniques.

Virtual Worlds as Proxy for Multi-Object Tracking Analysis

The paper "Virtual Worlds as Proxy for Multi-Object Tracking Analysis" investigates the utility of using synthetic, photo-realistic datasets to evaluate multi-object tracking (MOT) algorithms. By leveraging the advancements in computer graphics, the authors propose a method to generate dynamically labeled virtual worlds, serving as proxies for real-world environments, and introduce a new video dataset called Virtual KITTI.

Approach and Contributions

The paper's key contributions are fourfold:

1. Data Generation: The authors present a method to generate synthetic video datasets by cloning a few key sequences from real-world data. This cloning involves duplicating the camera paths, object arrangements, and other scene dynamics. The Virtual KITTI dataset exemplifies this approach, consisting of 35 synthetic videos derived from the KITTI benchmark, encompassing variations in weather, lighting, and camera perspectives.
2. Ground Truth Annotations: Leveraging computer graphics, the authors automate the generation of dense ground truth annotations, which include object detection, tracking, depth estimation, optical flow, and scene and instance segmentation. This method ensures high accuracy and consistency, counteracting the subjectivity and variability inherent in manual annotations.
3. Transferability Analysis: A critical aspect of the research is the evaluation of whether conclusions drawn from synthetic data can effectively transfer to real-world scenarios. This is achieved by comparing MOT performance metrics across real KITTI sequences and their synthetic clones, with minimal performance gaps observed.
4. Impact Assessment: The authors use the synthetic environments to quantitatively assess the sensitivity of MOT algorithms to various conditions (e.g., weather, lighting) while keeping other factors constant. This analysis highlights the limitations of existing models and the need for further research into robustness and generalization.

Experimental Results

Transferability

The experiments indicate that the performance of deep learning-based MOT algorithms remains consistent across real and cloned sequences. For instance, the MOTA (Multiple Object Tracking Accuracy) discrepancy between real and synthetic sequences was less than 0.5% on average for both the DP-MCF and MDP trackers. This close alignment underlines the viability of using synthetic data to draw practical conclusions about real-world performance.

Virtual Pre-Training

The research also demonstrates the benefits of virtual pre-training. Models initially trained on Virtual KITTI and subsequently fine-tuned on real data outperformed those trained solely on real data. This improvement is particularly significant for the DP-MCF tracker, suggesting that synthetic pre-training can enhance the robustness and performance of MOT algorithms.

Sensitivity to Environmental Changes

The paper's analysis of environmental impact reveals a substantial degradation in performance under conditions like fog and rain. For instance, MOTA dropped by over 45% for DP-MCF and 57% for MDP in foggy conditions. This finding underscores the necessity for more extensive and varied training datasets and improved domain adaptation techniques.

Implications and Future Work

The theoretical implications of this research extend to the broader field of computer vision, where synthetic data can help overcome the limitations of real-world data collection, such as cost, variability, and annotation scalability. Practically, the Virtual KITTI dataset provides a versatile tool for benchmarking and improving MOT algorithms under controlled yet varied conditions, which can facilitate better deployment strategies in autonomous systems and surveillance.

Future developments could involve expanding the dataset with additional scenes and object classes, such as pedestrians. Furthermore, exploring advanced domain adaptation methods will be crucial to bridge any remaining gaps between synthetic and real-world performance effectively.

Conclusion

This paper demonstrates that synthetic, photo-realistic datasets like Virtual KITTI are valuable for evaluating and enhancing multi-object tracking algorithms. The findings validate the small gap between virtual and real environments and emphasize the significance of addressing environmental variability for robust computer vision models.