Motion Segmentation of RGB-D Sequences: Combining Semantic and Motion Information Using Statistical Inference

Muthu, Sundaram; Tennakoon, Ruwan; Rathnayake, Tharindu; Hoseinnezhad, Reza; Suter, David; Bab–Hadiashar, Alireza

doi:10.1109/tip.2020.2984893

Cited by 18 publications

(16 citation statements)

References 66 publications

(101 reference statements)

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“…Muthu [15] correspondence matching that overcame the problem of over segmentation occurred in the moving parts of different objects. The developed model identified objects with similar motions thereby characterized motion using a statistical inference theory to assess similarities.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Once the position of wolf is obtained by using Eq. (14) and (15), best optimal value selection is important by using fitness value. The Best fitness value will be obtained only if the minimum distance of the clusters is also less.…”

Section: Kfcm Algorithmmentioning

confidence: 99%

“…The developed multi-sensor scheduler Algorithm used SBM-RGBD dataset for the experimentation that achieved Recall of 90.79 %, Specificity of 99.71%, Precision of 95.36%, F-measure of 93.02 %. In [15], the developed model identified objects with similar motions thereby characterized motion using a statistical inference theory to assess similarities. The developed Statistical Inference Theory model used SBM-RGBD dataset for the experimentation that achieved Recall of 85.87 %, Precision of 68.42 %, Fmeasure of 74.18%.…”

Section: Comparison Analysismentioning

confidence: 99%

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Moving Object Segmentation Using Level Set Algorithm with GWO-KFCM Clustering

Raghavulu¹,

Prasad²

2020

IJIES

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Video analytics is widely used to automatically analyze the videos to extract the required information and detect the various events object identification and traffic analysis. The segmentation of the image is referring to extracting the required region from an image. The major objective of the segmentation process is to cluster the images without being affected by the noises. The detection of the moving objects is a challenging task in video analysis due to the dynamic background of the video. The major drawback of the existing Kernel Fuzzy C-Means (KFCM) clustering is initialization of random centroids which increases the execution time to identify the segmented portions. In this research, the Grey Wolf Optimization (GWO) algorithm used to initialize the centroids of required clusters in KFCM and Level Set (LS) Algorithm is used to segment the objects in video sequence. The proposed KFCM-GWO-LS is implemented for moving and static object detection in the videos obtained from SBM-RGBD dataset. For object detection, determining central clusters are important which is performed by using KFCM. GWO helps in finding the best centroids clusters by matching with KFCM. The centroids clusters are segmented by using LS algorithm which undergoes over segmentation problem that is overcome by GWO. As all the three techniques are dependent on one another hybrid of all these techniques obtains better results. The proposed KFCM-GWO-LS is evaluated in terms of Recall, Specificity, Precision and F-measure and the experimental results showed that the proposed method improved the system performance from 0.3 % to 14.35 % compared to existing methods Multi-Sensor Scheduler Algorithm and Statistical Inference Theory model.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Kfcm Algorithmmentioning

confidence: 99%

Section: Comparison Analysismentioning

confidence: 99%

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Moving Object Segmentation Using Level Set Algorithm with GWO-KFCM Clustering

Raghavulu¹,

Prasad²

2020

IJIES

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“…Therefore, the RGB-D saliency detection using depth information is attracting more and more attention. Moreover, the effectiveness of depth information has been fully proved in other computer vision tasks, such as motion segmentation [ 7 ] and people re-identification [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Saliency Detection with Bilateral Absorbing Markov Chain Guided by Depth Information

Han

Liu

et al. 2021

Sensors

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The effectiveness of depth information in saliency detection has been fully proved. However, it is still worth exploring how to utilize the depth information more efficiently. Erroneous depth information may cause detection failure, while non-salient objects may be closer to the camera which also leads to erroneously emphasis on non-salient regions. Moreover, most of the existing RGB-D saliency detection models have poor robustness when the salient object touches the image boundaries. To mitigate these problems, we propose a multi-stage saliency detection model with the bilateral absorbing Markov chain guided by depth information. The proposed model progressively extracts the saliency cues with three level (low-, mid-, and high-level) stages. First, we generate low-level saliency cues by explicitly combining color and depth information. Then, we design a bilateral absorbing Markov chain to calculate mid-level saliency maps. In mid-level, to suppress boundary touch problem, we present the background seed screening mechanism (BSSM) for improving the construction of the two-layer sparse graph and better selecting background-based absorbing nodes. Furthermore, the cross-modal multi-graph learning model (CMLM) is designed to fully explore the intrinsic complementary relationship between color and depth information. Finally, to obtain a more highlighted and homogeneous saliency map in high-level, we structure a depth-guided optimization module which combines cellular automata and suppression-enhancement function pair. This optimization module refines the saliency map in color space and depth space, respectively. Comprehensive experiments on three challenging benchmark datasets demonstrate the effectiveness of our proposed method both qualitatively and quantitatively.

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“…Furthermore, registration between depth and RGB data allows traditional 2D background subtraction approaches to be supplemented with additional depth-based approaches (e.g., [ 23 , 24 ]), and further allows RGB-D data to be represented as 3D point clouds [ 25 ]. For example, reliably identifying and removing static background components such as roads and walls before modeling the background can result in both improved background subtraction and improved foreground segmentation using both 2D and RGB-D data; improvements that to our knowledge have only been realized through approaches that require additional data and computation, such as motion estimation between consecutive frames (e.g., [ 26 ]). Identifying static background components suffers from the same limitations as modeling the entire background using 2D data, suggesting that little benefit is afforded by first removing these background objects, then modeling the background.…”

Section: Introductionmentioning

confidence: 99%

Automatic Super-Surface Removal in Complex 3D Indoor Environments Using Iterative Region-Based RANSAC

Ebrahimi

Czarnuch

2021

Sensors

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Removing bounding surfaces such as walls, windows, curtains, and floor (i.e., super-surfaces) from a point cloud is a common task in a wide variety of computer vision applications (e.g., object recognition and human tracking). Popular plane segmentation methods such as Random Sample Consensus (RANSAC), are widely used to segment and remove surfaces from a point cloud. However, these estimators easily result in the incorrect association of foreground points to background bounding surfaces because of the stochasticity of randomly sampling, and the limited scene-specific knowledge used by these approaches. Additionally, identical approaches are generally used to detect bounding surfaces and surfaces that belong to foreground objects. Detecting and removing bounding surfaces in challenging (i.e., cluttered and dynamic) real-world scene can easily result in the erroneous removal of points belonging to desired foreground objects such as human bodies. To address these challenges, we introduce a novel super-surface removal technique for 3D complex indoor environments. Our method was developed to work with unorganized data captured from commercial depth sensors and supports varied sensor perspectives. We begin with preprocessing steps and dividing the input point cloud into four overlapped local regions. Then, we apply an iterative surface removal approach to all four regions to segment and remove the bounding surfaces. We evaluate the performance of our proposed method in terms of four conventional metrics: specificity, precision, recall, and F1 score, on three generated datasets representing different indoor environments. Our experimental results demonstrate that our proposed method is a robust super-surface removal and size reduction approach for complex 3D indoor environments while scoring the four evaluation metrics between 90% and 99%.

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Motion Segmentation of RGB-D Sequences: Combining Semantic and Motion Information Using Statistical Inference

Cited by 18 publications

References 66 publications

Moving Object Segmentation Using Level Set Algorithm with GWO-KFCM Clustering

Moving Object Segmentation Using Level Set Algorithm with GWO-KFCM Clustering

Saliency Detection with Bilateral Absorbing Markov Chain Guided by Depth Information

Automatic Super-Surface Removal in Complex 3D Indoor Environments Using Iterative Region-Based RANSAC

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