Video Object Segmentation and Tracking: A Survey

Abstract

Object segmentation and object tracking are fundamental research area in the computer vision community. These two topics are diffcult to handle some common challenges, such as occlusion, deformation, motion blur, and scale variation. The former contains heterogeneous object, interacting object, edge ambiguity, and shape complexity. And the latter suffers from difficulties in handling fast motion, out-of-view, and real-time processing. Combining the two problems of video object segmentation and tracking (VOST) can overcome their respective difficulties and improve their performance. VOST can be widely applied to many practical applications such as video summarization, high definition video compression, human computer interaction, and autonomous vehicles. This article aims to provide a comprehensive review of the state-of-the-art tracking methods, and classify these methods into different categories, and identify new trends. First, we provide a hierarchical categorization existing approaches, including unsupervised VOS, semi-supervised VOS, interactive VOS, weakly supervised VOS, and segmentation-based tracking methods. Second, we provide a detailed discussion and overview of the technical characteristics of the different methods. Third, we summarize the characteristics of the related video dataset, and provide a variety of evaluation metrics. Finally, we point out a set of interesting future works and draw our own conclusions.

References in corpus (11)

• Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
• Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials
• MOT16: A Benchmark for Multi-Object Tracking
• SfM-Net: Learning of Structure and Motion from Video
• The 2018 DAVIS Challenge on Video Object Segmentation
• FusionSeg: Learning to combine motion and appearance for fully automatic segmention of generic objects in videos
• Fast Online Object Tracking and Segmentation: A Unifying Approach
• Learning Video Object Segmentation with Visual Memory
• Interactive Video Object Segmentation in the Wild
• Learning to Segment Instances in Videos with Spatial Propagation Network
• Video Object Segmentation using Tracked Object Proposals

Cited by in corpus (6)

• A Survey of Human-in-the-loop for Machine Learning
• Cell Segmentation and Tracking using CNN-Based Distance Predictions and a Graph-Based Matching Strategy
• Depth Map Estimation of Dynamic Scenes Using Prior Depth Information
• TagMe: GPS-Assisted Automatic Object Annotation in Videos
• Perceptual Consistency in Video Segmentation
• Monitoring of people entering and exiting private areas using Computer Vision