PEDESTRIAN SEGMENTATION FROM COMPLEX BACKGROUND BASED ON PREDEFINED POSE FIELDS AND PROBABILISTIC RELAXATION

The wide use of cameras enables the availability of a large amount of image frames that can be used for people counting or to monitor crowds or single individuals for security purposes. These applications require both, object detection and tracking. This task has shown to be challenging due to problems such as occlusion, deformation, motion blur, and scale variation. One alternative to perform tracking is based on the comparison of features extracted for the individual objects from the image. For this purpose, it is necessary to identify the object of interest, a human image, from the rest of the scene. This paper introduces a method to perform the separation of human bodies from images with changing backgrounds. The method is based on image segmentation, the analysis of the possible pose, and a final refinement step based on probabilistic relaxation. It is the first work we are aware that probabilistic fields computed from human pose figures are combined with an improvement step of relaxation for pedestrian segmentation. The proposed method is evaluated using different image series and the results show that it can work efficiently, but it is dependent on some parameters to be set according to the image contrast and scale. Tests show accuracies above 71%. The method performs well in other datasets, where it achieves results comparable to stateof-the-art approaches

Unsupervised Online Video Object Segmentation with Motion Property Understanding

Unsupervised video object segmentation aims to automatically segment moving objects over an unconstrained video without any user annotation. So far, only few unsupervised online methods have been reported in literature and their performance is still far from satisfactory, because the complementary information from future frames cannot be processed under online setting. To solve this challenging problem, in this paper, we propose a novel Unsupervised Online Video Object Segmentation (UOVOS) framework by construing the motion property to mean moving in concurrence with a generic object for segmented regions. By incorporating salient motion detection and object proposal, a pixel-wise fusion strategy is developed to effectively remove detection noise such as dynamic background and stationary objects. Furthermore, by leveraging the obtained segmentation from immediately preceding frames, a forward propagation algorithm is employed to deal with unreliable motion detection and object proposals. Experimental results on several benchmark datasets demonstrate the efficacy of the proposed method. Compared to the state-of-the-art unsupervised online segmentation algorithms, the proposed method achieves an absolute gain of 6.2%. Moreover, our method achieves better performance than the best unsupervised offline algorithm on the DAVIS-2016 benchmark dataset. Our code is available on the project website: https://github.com/visiontao/uovos