Authors:
N. Wondimu
1
;
2
;
U. Visser
3
and
C. Buche
1
;
4
Affiliations:
1
Lab-STICC, Brest National School of Engineering, 29280, Plouzané, France
;
2
School of Information Technology and Engineering, Addis Ababa University, Addis Ababa, Ethiopia
;
3
University of Miami, Florida, U.S.A.
;
4
IRL CROSSING, CNRS, Adelaide, Australia
Keyword(s):
Moving Object Detection, Frame Differencing, Object Segmentation, XY-shift Frame Differencing, Three-Frame Differencing.
Abstract:
Motion out-weights other low-level saliency features in attracting human attention and defining region of interests. The ability to effectively identify moving objects in a sequence of frames help to solve important computer vision problems, such as moving object detection and segmentation. In this paper, we propose a novel frame differencing technique along with a simple three-stream encoder-decoder architecture to effectively and efficiently detect and segment moving objects in a sequence of frames. Our frame differencing component incorporates a novel self-differencing technique, which we call XY-shift frame differencing, and an improved three-frame differencing technique. We fuse the feature maps from the raw frame and the two outputs of our frame differencing component, and fed them to our transfer-learning based convolutional base, VGG-16. The result from this sub-component is further deconvolved and the desired segmentation map is produced. The effectiveness of our model is ev
aluated using the re-labeled multi-spectral CDNet-2014 dataset for motion segmentation. The qualitative and quantitative results show that our technique achieves effective and efficient moving object detection and segmentation results relative to the state-of-the-art methods.
(More)