Saliency detection with moving camera via background model completion

by   Yupei Zhang, et al.

To detect saliency in video is a fundamental step in many computer vision systems. Saliency is the significant target(s) in the video. The object of interest is further analyzed for high-level applications. The segregation of saliency and the background can be made if they exhibit different visual cues. Therefore, saliency detection is often formulated as background subtraction. However, saliency detection is challenging. For instance, dynamic background can result in false positive errors. In another scenario, camouflage will lead to false negative errors. With moving camera, the captured scenes are even more complicated to handle. We propose a new framework, called saliency detection via background model completion (SD-BMC), that comprises of a background modeler and the deep learning background/foreground segmentation network. The background modeler generates an initial clean background image from a short image sequence. Based on the idea of video completion, a good background frame can be synthesized with the co-existence of changing background and moving objects. We adopt the background/foreground segmenter, although pre-trained with a specific video dataset, can also detect saliency in unseen videos. The background modeler can adjust the background image dynamically when the background/foreground segmenter output deteriorates during processing of a long video. To the best of our knowledge, our framework is the first one to adopt video completion for background modeling and saliency detection in videos captured by moving camera. The results, obtained from the PTZ videos, show that our proposed framework outperforms some deep learning-based background subtraction models by 11 also outperforms many high ranking background subtraction methods by more than 3


page 6

page 7

page 12

page 13

page 15

page 16


Saliency Enhancement using Superpixel Similarity

Saliency Object Detection (SOD) has several applications in image analys...

CVABS: Moving Object Segmentation with Common Vector Approach for Videos

Background modelling is a fundamental step for several real-time compute...

OmnimatteRF: Robust Omnimatte with 3D Background Modeling

Video matting has broad applications, from adding interesting effects to...

Video Segmentation via Diffusion Bases

Identifying moving objects in a video sequence, which is produced by a s...

Background Subtraction in Real Applications: Challenges, Current Models and Future Directions

Computer vision applications based on videos often require the detection...

Segmentation Rectification for Video Cutout via One-Class Structured Learning

Recent works on interactive video object cutout mainly focus on designin...

Adaptive Background Matting Using Background Matching

Due to the difficulty of solving the matting problem, lots of methods us...

Please sign up or login with your details

Forgot password? Click here to reset