Video Instance Segmentation (1905.04804v4)

Abstract: In this paper we present a new computer vision task, named video instance segmentation. The goal of this new task is simultaneous detection, segmentation and tracking of instances in videos. In words, it is the first time that the image instance segmentation problem is extended to the video domain. To facilitate research on this new task, we propose a large-scale benchmark called YouTube-VIS, which consists of 2883 high-resolution YouTube videos, a 40-category label set and 131k high-quality instance masks. In addition, we propose a novel algorithm called MaskTrack R-CNN for this task. Our new method introduces a new tracking branch to Mask R-CNN to jointly perform the detection, segmentation and tracking tasks simultaneously. Finally, we evaluate the proposed method and several strong baselines on our new dataset. Experimental results clearly demonstrate the advantages of the proposed algorithm and reveal insight for future improvement. We believe the video instance segmentation task will motivate the community along the line of research for video understanding.

• The paper introduces video instance segmentation, unifying detection, segmentation, and tracking within a single framework.
• It presents MaskTrack R-CNN, an extended Mask R-CNN that integrates a tracking branch with appearance and spatial consistency cues.
• The study demonstrates superior performance on the YouTube-VIS dataset, highlighting improvements in temporal tracking and precision.

Video Instance Segmentation: Extending Instance Segmentation to the Video Domain

The paper, titled "Video Instance Segmentation," introduces a novel task in computer vision that unifies detection, segmentation, and tracking of object instances within videos. This research extends the established domain of image instance segmentation into the temporal dimension, thus addressing the challenge of tracking object instances across frames in addition to segmenting them.

Introduction and Problem Definition

The task of video instance segmentation is formally defined as the detection, segmentation, and tracking of object instances in videos. Unlike video object segmentation, which has been the subject of previous studies, video instance segmentation requires the recognition of object categories, an aspect that adds complexity and necessitates a more comprehensive dataset that includes category labels and tracking information.

The authors introduce the YouTube-VIS dataset, which facilitates evaluation and development in this nascent area. It comprises 2,883 high-resolution YouTube videos annotated with 131,000 instance masks across 40 categories. This dataset serves not only the task of video instance segmentation but also other related applications such as video semantic segmentation and object detection.

The MaskTrack R-CNN Algorithm

A significant contribution of the paper is the development of MaskTrack R-CNN, which extends the Mask R-CNN framework. The addition of a tracking branch, designed to manage the temporal consistency of instance identities, distinguishes this algorithm. This component utilizes appearance similarity and integrates auxiliary cues like semantic consistency and spatial correlation, which collectively enhance the tracking precision.

The model processes video frames sequentially and relies on an external memory for storing features of previously detected instances. This facilitates tracking across frames by matching new detections with stored features. The system’s efficacy is measured using modified average precision and recall metrics tailored for video instance segmentation.

Experimental Results and Discussion

MaskTrack R-CNN was evaluated using YouTube-VIS, demonstrating superior performance over several baselines, including novel implementations of OSMN and FEELVOS adapted for this task. The comparative analysis revealed that the integration of appearance, detection confidence, and spatial cues improves tracking accuracy. Ablation studies corroborated the significance of semantic and spatial consistency cues.

A key insight from the oracle experiments highlights the pivotal role of precise image-level detection. Prospects for future research include leveraging spatial-temporal features for object proposal, implementing end-to-end trainable matching criteria, and integrating motion information to bolster instance tracking.

Implications and Future Directions

The introduction of video instance segmentation as a formal task opens new avenues in video understanding, impacting areas such as autonomous driving and augmented reality where real-time instance-level understanding of dynamic scenes is crucial. The YouTube-VIS dataset provides a robust benchmark for further advancements in this domain. As researchers explore these directions, the potential for novel architectures and algorithms that harness temporal and spatial dynamics in video data promises to significantly enhance the capabilities of visual AI systems.

In conclusion, this paper establishes foundational work in video instance segmentation by providing essential tools and methodologies that will guide and inspire future research developments in video-based computer vision tasks.