Granular Flow Graph, Adaptive Rule Generation and Tracking
01 Dec 2017-IEEE Transactions on Systems, Man, and Cybernetics (IEEE Trans Cybern)-Vol. 47, Iss: 12, pp 4096-4107
TL;DR: A new method of adaptive rule generation in granular computing framework is described based on rough rule base and granular flow graph, and applied for video tracking, and it is shown that the neighborhood granulation provides a balanced tradeoff between speed and accuracy as compared to pixel level computation.
Abstract: A new method of adaptive rule generation in granular computing framework is described based on rough rule base and granular flow graph, and applied for video tracking. In the process, several new concepts and operations are introduced, and methodologies formulated with superior performance. The flow graph enables in defining an intelligent technique for rule base adaptation where its characteristics in mapping the relevance of attributes and rules in decision-making system are exploited. Two new features, namely, expected flow graph and mutual dependency between flow graphs are defined to make the flow graph applicable in the tasks of both training and validation. All these techniques are performed in neighborhood granular level. A way of forming spatio-temporal 3-D granules of arbitrary shape and size is introduced. The rough flow graph-based adaptive granular rule-based system, thus produced for unsupervised video tracking, is capable of handling the uncertainties and incompleteness in frames, able to overcome the incompleteness in information that arises without initial manual interactions and in providing superior performance and gaining in computation time. The cases of partial overlapping and detecting the unpredictable changes are handled efficiently. It is shown that the neighborhood granulation provides a balanced tradeoff between speed and accuracy as compared to pixel level computation. The quantitative indices used for evaluating the performance of tracking do not require any information on ground truth as in the other methods. Superiority of the algorithm to nonadaptive and other recent ones is demonstrated extensively.
Citations
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TL;DR: This study identifies the principles of Granular Computing, shows how information granules are constructed and subsequently used in describing relationships present among the data, and advocates that the level of abstraction can be flexibly adjusted through Granular computing.
Abstract: In the plethora of conceptual and algorithmic developments supporting data analytics and system modeling, humancentric pursuits assume a particular position owing to ways they emphasize and realize interaction between users and the data. We advocate that the level of abstraction, which can be flexibly adjusted, is conveniently realized through Granular Computing. Granular Computing is concerned with the development and processing information granules – formal entities which facilitate a way of organizing knowledge about the available data and relationships existing there. This study identifies the principles of Granular Computing, shows how information granules are constructed and subsequently used in describing relationships present among the data.
108 citations
TL;DR: In this article, the authors provide a comprehensive overview of object detection and tracking using deep learning (DL) networks and compare the performance of different object detectors and trackers, including the recent development in granulated DL models.
Abstract: Object detection and tracking is one of the most important and challenging branches in computer vision, and have been widely applied in various fields, such as health-care monitoring, autonomous driving, anomaly detection, and so on. With the rapid development of deep learning (DL) networks and GPU’s computing power, the performance of object detectors and trackers has been greatly improved. To understand the main development status of object detection and tracking pipeline thoroughly, in this survey, we have critically analyzed the existing DL network-based methods of object detection and tracking and described various benchmark datasets. This includes the recent development in granulated DL models. Primarily, we have provided a comprehensive overview of a variety of both generic object detection and specific object detection models. We have enlisted various comparative results for obtaining the best detector, tracker, and their combination. Moreover, we have listed the traditional and new applications of object detection and tracking showing its developmental trends. Finally, challenging issues, including the relevance of granular computing, in the said domain are elaborated as a future scope of research, together with some concerns. An extensive bibliography is also provided.
104 citations
TL;DR: An attribute-driven granular model (AGrM) under a machine-learning scheme that achieved comparable pinch recognition accuracy but was of lowest computational cost and highest force grand prediction accuracy.
Abstract: Fine multifunctional prosthetic hand manipulation requires precise control on the pinch-type and the corresponding force, and it is a challenge to decode both aspects from myoelectric signals. This paper proposes an attribute-driven granular model (AGrM) under a machine-learning scheme to solve this problem. The model utilizes the additionally captured attribute as the latent variable for a supervised granulation procedure. It was fulfilled for EMG-based pinch-type classification and the fingertip force grand prediction. In the experiments, 16 channels of surface electromyographic signals (i.e., main attribute) and continuous fingertip force (i.e., subattribute) were simultaneously collected while subjects performing eight types of hand pinches. The use of AGrM improved the pinch-type recognition accuracy to around 97.2% by 1.8% when constructing eight granules for each grasping type and received more than 90% force grand prediction accuracy at any granular level greater than six. Further, sensitivity analysis verified its robustness with respect to different channel combination and interferences. In comparison with other clustering-based granulation methods, AGrM achieved comparable pinch recognition accuracy but was of lowest computational cost and highest force grand prediction accuracy.
31 citations
Additional excerpts
...groups, classes or clusters of a universe, which is closely related to the cognitive strategy of human being in problem solving and it is technically transferable to the design of human-centric intelligent systems [17], which has been applied in image-based crowd segmentation [18], longterm prediction model for the energy system [19], [20], video based object tracking [21] and principle curve extraction [22], etc....
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TL;DR: Theoretical analysis and experimental results show that FGKNN and BFGKNN have better performance than that of the methods mentioned above if the appropriate parameters are given.
Abstract: K-nearest neighbor (KNN) is a classic classifier, which is simple and effective. Adaboost is a combination of several weak classifiers as a strong classifier to improve the classification effect. These two classifiers have been widely used in the field of machine learning. In this paper, based on information fuzzy granulation, KNN and Adaboost, we propose two algorithms, a fuzzy granule K-nearest neighbor (FGKNN) and a boosted fuzzy granule K-nearest neighbor (BFGKNN), for classification. By introducing granular computing, we normalize the process of solving problem as a structured and hierarchical process. Structured information processing is focused, so the performance including accuracy and robust can be enhanced to data classification. First, a fuzzy set is introduced, and an atom attribute fuzzy granulation is performed on samples in the classified system to form fuzzy granules. Then, a fuzzy granule vector is created by multiple attribute fuzzy granules. We design the operators and define the measure of fuzzy granule vectors in the fuzzy granule space. And we also prove the monotonic principle of the distance of fuzzy granule vectors. Furthermore, we also give the definition of the concept of K-nearest neighbor fuzzy granule vector and present FGKNN algorithm and BFGKNN algorithm. Finally, we compare the performance among KNN, Back Propagation Neural Network (BPNN), Support Vector Machine (SVM), Logistic Regression (LR), FGKNN and BFGKNN on UCI data sets. Theoretical analysis and experimental results show that FGKNN and BFGKNN have better performance than that of the methods mentioned above if the appropriate parameters are given.
31 citations
TL;DR: The article deals with the problems of motion detection, object recognition, and scene description using deep learning in the framework of granular computing and Z-numbers and shows a balanced trade-off between speed and accuracy as compared to pixel level learning in tracking and recognition.
Abstract: The article deals with the problems of motion detection, object recognition, and scene description using deep learning in the framework of granular computing and Z-numbers. Since deep learning is computationally intensive, whereas granular computing, on the other hand, leads to computation gain, a judicious integration of their merits is made so as to make the learning mechanism computationally efficient. Further, it is shown how the concept of z-numbers can be used to quantify the abstraction of semantic information in interpreting a scene, where subjectivity is of major concern, through recognition of its constituting objects. The system, thus developed, involves recognition of both static objects in the background and moving objects in foreground separately. Rough set theoretic granular computing is adopted where rough lower and upper approximations are used in defining object and background models. During deep learning, instead of scanning the entire image pixel by pixel in the convolution layer, we scan only the representative pixel of each granule. This results in a significant gain in computation time. Arbitrary-shaped and sized granules, as expected, perform better than regular-shaped rectangular granules or fixed-sized granules. The method of tracking is able to deal efficiently with various challenging cases, e.g., tracking partially overlapped objects and suddenly appeared objects. Overall, the granulated system shows a balanced trade-off between speed and accuracy as compared to pixel level learning in tracking and recognition. The concept of using Z-numbers, in providing a granulated linguistic description of a scene, is unique. This gives a more natural interpretation of object recognition in terms of certainty toward scene understanding.
19 citations
References
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TL;DR: A robust state-based structured support vector machine (SVM) tracking algorithm combined with incremental principal component analysis (PCA) that directly learns and predicts the object's states and not the 2-D translation transformation during tracking.
Abstract: In this paper, we propose a robust state-based structured support vector machine (SVM) tracking algorithm combined with incremental principal component analysis (PCA). Different from the current structured SVM for tracking, our method directly learns and predicts the object’s states and not the 2-D translation transformation during tracking. We define the object’s virtual state to combine the state-based structured SVM and incremental PCA. The virtual state is considered as the most confident state of the object in every frame. The incremental PCA is used to update the virtual feature vector corresponding to the virtual state and the principal subspace of the object’s feature vectors. In order to improve the accuracy of the prediction, all the feature vectors are projected onto the principal subspace in the learning and prediction process of the state-based structured SVM. Experimental results on several challenging video sequences validate the effectiveness and robustness of our approach.
44 citations
"Granular Flow Graph, Adaptive Rule ..." refers background in this paper
...A few unsupervised approaches for tracking are recently proposed with statistical modeling of object motion [20], [23], [24]....
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01 Feb 2011
TL;DR: A tracking-based surveillance system that is capable of tracking multiple moving objects, with almost real-time response, through the effective cooperation of multiple pan-tilt cameras, and a hierarchical camera selection and task assignment strategy, known as the online position strategy, to integrate all of the distributed camera agents are presented.
Abstract: This paper presents a tracking-based surveillance system that is capable of tracking multiple moving objects, with almost real-time response, through the effective cooperation of multiple pan-tilt cameras. To construct this surveillance system, the distributed camera agent, which tracks multiple moving objects independently, is first developed. The particle filter is extended with target depth estimate to track multiple targets that may overlap with one another. A strategy to select the suboptimal camera action is then proposed for a camera mounted on a pan-tilt platform that has been assigned to track multiple targets within its limited field of view simultaneously. This strategy is based on the mutual information and the Monte Carlo method to maintain coverage of the tracked targets. Finally, for a surveillance system with a small number of active cameras to effectively monitor a wide space, this system is aimed to maximize the number of targets to be tracked. We further propose a hierarchical camera selection and task assignment strategy, known as the online position strategy, to integrate all of the distributed camera agents. The overall performance of the multicamera surveillance system has been verified with computer simulations and extensive experiments.
42 citations
"Granular Flow Graph, Adaptive Rule ..." refers background in this paper
...Some such examples are, multiple cameras [27], PTZ camera [28], and Kinect sensor [29], [30]....
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TL;DR: 3D skeleton tracking technique using a depth camera known as a Kinect sensor with the ability to approximate human poses to be captured, reconstructed and displayed 3D skeleton in the virtual scene using OPENNI, NITE Primesense and CHAI3D open source libraries is proposed.
Abstract: Current research on skeleton tracking techniques focus on image processing in conjunction with a video camera constrained by bones and joint movement detection limits. The paper proposed 3D skeleton tracking technique using a depth camera known as a Kinect sensor with the ability to approximate human poses to be captured, reconstructed and displayed 3D skeleton in the virtual scene using OPENNI, NITE Primesense and CHAI3D open source libraries. The technique could perform the bone joint movement detections in real time with correct position tracking and display a 3D skeleton in a virtual environment with abilities to control 3D character movements for the future research.
35 citations
"Granular Flow Graph, Adaptive Rule ..." refers methods in this paper
...There are many sequences with different types of movement of human hands which are sensed by Kinect sensor and several surveillance scenarios with movement of people obtained from IR sensor....
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...The sequences M_9 and M_7 represent two hand movement videos sensed by Kinect sensor....
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...The datasets that are used in this paper are sensed either by infra red (IR) sensor [31] (different types of surveillance) or by Kinect [32] sensor (different types of hand movements)....
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...D neighborhood granules (Section II-A1), unlike the earlier work [33] using only Kinect data and 1-...
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...Moreover, these indices involve both Kinect and IR sensed data and the proposed 3-...
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TL;DR: A novel solution for particle filtering on general graphs that relies on a partial-order relation in an antichain decomposition that forms a high-order Markov chain over the partitioned graph to derive a closed-form sequential updating scheme for conditional density propagation.
Abstract: In this paper, we develop a novel solution for particle filtering on general graphs. We provide an exact solution for particle filtering on directed cycle-free graphs. The proposed approach relies on a partial-order relation in an antichain decomposition that forms a high-order Markov chain over the partitioned graph. We subsequently derive a closed-form sequential updating scheme for conditional density propagation using particle filtering on directed cycle-free graphs. We also provide an approximate solution for particle filtering on general graphs by splitting graphs with cycles into multiple directed cycle-free subgraphs. We then use the sequential updating scheme by alternating among the directed cycle-free subgraphs to obtain an estimate of the density propagation. We rely on the proposed method for particle filtering on general graphs for two video tracking applications: 1) object tracking using high-order Markov chains; and 2) distributed multiple object tracking based on multi-object graphical interaction models. Experimental results demonstrate the improved performance of the proposed approach to particle filtering on graphs compared with existing methods for video tracking.
34 citations
"Granular Flow Graph, Adaptive Rule ..." refers methods in this paper
...The values of these indices obtained for tracking of M_9 sequence with the five methods [SPG (blue, “....
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...It can be noticed from the table that the computation time is the lowest for the proposed method NRBFG with accuracy better than PLS, SPG, and GMOT, but slightly worse than CEMT and LLAS....
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...10, the red tracker shows the results obtained by NRBFG, the green tracker shows the results of PLS, the blue tracker shows the results of SPG, the yellow tracker shows the results obtained by CEMT, the magenta tracker shows the results obtained by LLAS, and the black tracker shows the results of GMOT....
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...The tracking results for SPG are satisfactory for all of the four sequences, but the tracker fails to cover the entire moving objects, whereas both CEMT and LLAS give very satisfactory results even with multiple moving elements....
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...The performance of the other two methods is satisfactory for these two frames, though SPG can not cover the entire objects resulting in higher ℵI and ED values (same is reflected by CD) compared to that of LLAS, NRBFG, and CEMT....
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TL;DR: A novel approach for background subtraction in bitstreams encoded in the Baseline profile of H.264/AVC is presented and a low-complexity technique for color comparison is proposed which enables to obtain pixel-resolution segmentation at a negligible computational cost as compared to those of classical pixel-based approaches.
Abstract: The H.264/Advanced Video Coding (AVC) is the industry standard in network surveillance offering the lowest bitrate for a given perceptual quality among any MPEG or proprietary codecs. This paper presents a novel approach for background subtraction in bitstreams encoded in the Baseline profile of H.264/AVC. Temporal statistics of the proposed feature vectors, describing macroblock units in each frame, are used to select potential candidates containing moving objects. From the candidate macroblocks, foreground pixels are determined by comparing the colors of corresponding pixels pair-wise with a background model. The basic contribution of the current work compared to the related approaches is that, it allows each macroblock to have a different quantization parameter, in view of the requirements in variable as well as constant bit-rate applications. Additionally, a low-complexity technique for color comparison is proposed which enables us to obtain pixel-resolution segmentation at a negligible computational cost as compared to those of classical pixel-based approaches. Results showing striking comparison against those of proven state-of-the-art pixel domain algorithms are presented over a diverse set of standardized surveillance sequences.
30 citations