Performance evaluation of detecting moving objects using graph cut segmentation

2014 ◽  
Author(s):  
A. Ramya ◽  
P. Raviraj
Keyword(s):  
Performance Evaluation ◽  
Moving Objects ◽  
Graph Cut ◽  
Graph Cut Segmentation

Detecting and Tracking Segmentation of Moving Objects Using Graph Cut Algorithm

2018 ◽  
pp. 177-192
Author(s):  
Raviraj Pandian ◽  
Ramya A.
Keyword(s):  
Object Detection ◽  
Moving Objects ◽  
Graph Cut ◽  
Background Model ◽  
Camera Motion ◽  
Current Frame ◽  
Outdoor Environments ◽  
Previous Frame ◽  
Indoor And Outdoor Environments ◽  
Graph Cut Segmentation

Real-time moving object detection, classification, and tracking capabilities are presented with system operates on both color and gray-scale video imagery from a stationary camera. It can handle object detection in indoor and outdoor environments and under changing illumination conditions. Object detection in a video is usually performed by object detectors or background subtraction techniques. The proposed method determines the threshold automatically and dynamically depending on the intensities of the pixels in the current frame. In this method, it updates the background model with learning rate depending on the differences of the pixels in the background model of the previous frame. The graph cut segmentation-based region merging algorithm approaches achieve both segmentation and optical flow computation accurately and they can work in the presence of large camera motion. The algorithm makes use of the shape of the detected objects and temporal tracking results to successfully categorize objects into pre-defined classes like human, human group, and vehicle.

scholarly journals Pedestrian segmentation based on a spatio-temporally consistent graph-cut with optimal transport

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Yang Yu ◽  
Yasushi Makihara ◽  
Yasushi Yagi
Keyword(s):  
Optimal Transport ◽  
Graph Cut ◽  
Temporal Consistency ◽  
Semantic Level ◽  
Spatial Consistency ◽  
Segmentation Accuracy ◽  
Spatio Temporal ◽  
Accuracy Indices ◽  
Graph Cut Segmentation ◽  
Data Term

AbstractWe address a method of pedestrian segmentation in a video in a spatio-temporally consistent way. For this purpose, given a bounding box sequence of each pedestrian obtained by a conventional pedestrian detector and tracker, we construct a spatio-temporal graph on a video and segment each pedestrian on the basis of a well-established graph-cut segmentation framework. More specifically, we consider three terms as an energy function for the graph-cut segmentation: (1) a data term, (2) a spatial pairwise term, and (3) a temporal pairwise term. To maintain better temporal consistency of segmentation even under relatively large motions, we introduce a transportation minimization framework that provides a temporal correspondence. Moreover, we introduce the edge-sticky superpixel to maintain the spatial consistency of object boundaries. In experiments, we demonstrate that the proposed method improves segmentation accuracy indices, such as the average and weighted intersection of union on TUD datasets and the PETS2009 dataset at both the instance level and semantic level.

scholarly journals Investigating the Relevance of Graph Cut Parameter on Interactive and Automatic Cell Segmentation

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Kazeem Oyeyemi Oyebode ◽  
Shengzhi Du ◽  
Barend Jacobus van Wyk ◽  
Karim Djouani
Keyword(s):  
Statistical Analysis ◽  
Significant Role ◽  
Energy Function ◽  
Automatic Segmentation ◽  
Graph Cut ◽  
Cell Segmentation ◽  
Medical Field ◽  
Segmentation Strategy ◽  
Segmentation Accuracy ◽  
Graph Cut Segmentation

Graph cut segmentation provides a platform to analyze images through a global segmentation strategy, and as a result of this, it has gained a wider acceptability in many interactive and automatic segmentation fields of application, such as the medical field. The graph cut energy function has a parameter that is tuned to ensure that the output is neither oversegmented (shrink bias) nor undersegmented. Models have been proposed in literature towards the improvement of graph cut segmentation, in the context of interactive and automatic cell segmentation. Along this line of research, the graph cut parameter has been leveraged, while in some instances, it has been ignored. Therefore, in this work, the relevance of graph cut parameter on both interactive and automatic cell segmentation is investigated. Statistical analysis, based on F1 score, of three publicly available datasets of cells, suggests that the graph cut parameter plays a significant role in improving the segmentation accuracy of the interactive graph cut than the automatic graph cut.

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