Object Detection, Tracking, and Motion Segmentation for Object-level Video Segmentation

Abstract

We present an approach for object segmentation in videos that combines frame-level object detection with concepts from object tracking and motion segmentation. The approach extracts temporally consistent object tubes based on an off-the-shelf detector. Besides the class label for each tube, this provides a location prior that is independent of motion. For the final video segmentation, we combine this information with motion cues. The method overcomes the typical problems of weakly supervised/unsupervised video segmentation, such as scenes with no motion, dominant camera motion, and objects that move as a unit. In contrast to most tracking methods, it provides an accurate, temporally consistent segmentation of each object. We report results on four video segmentation datasets: YouTube Objects, SegTrackv2, egoMotion, and FBMS.

References in corpus (1)

• Fully Convolutional Networks for Semantic Segmentation

Cited by in corpus (4)

• MODNet: Moving Object Detection Network with Motion and Appearance for Autonomous Driving
• Weakly Supervised Semantic Segmentation using Web-Crawled Videos
• RST-MODNet: Real-time Spatio-temporal Moving Object Detection for Autonomous Driving
• Leveraging Tacit Information Embedded in CNN Layers for Visual Tracking