Dealing with sequences in the RGBDT space
This work addresses the challenge of incorporating temporal information for moving object segmentation in computer vision, which is incremental as it builds on existing methods by adding depth and thermal data.
The paper tackles the problem of segmenting moving objects in image sequences by introducing a probabilistic non-parametric model that integrates multiple cues from RGBDT data, demonstrating improved segmentation accuracy through experiments on a novel dataset.
Most of the current research in computer vision is focused on working with single images without taking in account temporal information. We present a probabilistic non-parametric model that mixes multiple information cues from devices to segment regions that contain moving objects in image sequences. We prepared an experimental setup to show the importance of using previous information for obtaining an accurate segmentation result, using a novel dataset that provides sequences in the RGBDT space. We label the detected regions ts with a state-of-the-art human detector. Each one of the detected regions is at least marked as human once.