Hierarchical Clustering of Time Series Based on Linear Information Granules

Time series clustering is one of the main tasks in time series data mining. In this paper, a new time series clustering algorithm is proposed based on linear information granules. First, we improve the identification method of fluctuation points using threshold set, which represents the main trend information of the original time series. Then using fluctuation points as segmented nodes, we segment the original time series into several information granules, and linear function is used to represent the information granules. With information granulation, a granular time series consisting of several linear information granules replaces the original time series. In order to cluster time series, we then propose a linear information granules based segmented matching distance measurement (LIG_SMD) to calculate the distance between every two granular time series. In addition, hierarchical clustering method is applied based on the new distance (LIG_SMD_HC) to get clustering results. Finally, some public and real datasets about time series are experimented to examine the effectiveness of the proposed algorithm. Specifically, Euclidean distance based hierarchical clustering (ED_HC) and Dynamic Time Warping distance based hierarchical clustering (DTW_HC) are used as the compared algorithms. Our results show that LIG_SMD_HC is better than ED_HC and DTW_HC in terms of F-Measure and Accuracy

Lost in Time: Temporal Analytics for Long-Term Video Surveillance

Video surveillance is a well researched area of study with substantial work done in the aspects of object detection, tracking and behavior analysis. With the abundance of video data captured over a long period of time, we can understand patterns in human behavior and scene dynamics through data-driven temporal analytics. In this work, we propose two schemes to perform descriptive and predictive analytics on long-term video surveillance data. We generate heatmap and footmap visualizations to describe spatially pooled trajectory patterns with respect to time and location. We also present two approaches for anomaly prediction at the day-level granularity: a trajectory-based statistical approach, and a time-series based approach. Experimentation with one year data from a single camera demonstrates the ability to uncover interesting insights about the scene and to predict anomalies reasonably well.Comment: To Appear in Springer LNE

Computing Similarity between a Pair of Trajectories

With recent advances in sensing and tracking technology, trajectory data is becoming increasingly pervasive and analysis of trajectory data is becoming exceedingly important. A fundamental problem in analyzing trajectory data is that of identifying common patterns between pairs or among groups of trajectories. In this paper, we consider the problem of identifying similar portions between a pair of trajectories, each observed as a sequence of points sampled from it. We present new measures of trajectory similarity --- both local and global --- between a pair of trajectories to distinguish between similar and dissimilar portions. Our model is robust under noise and outliers, it does not make any assumptions on the sampling rates on either trajectory, and it works even if they are partially observed. Additionally, the model also yields a scalar similarity score which can be used to rank multiple pairs of trajectories according to similarity, e.g. in clustering applications. We also present efficient algorithms for computing the similarity under our measures; the worst-case running time is quadratic in the number of sample points. Finally, we present an extensive experimental study evaluating the effectiveness of our approach on real datasets, comparing with it with earlier approaches, and illustrating many issues that arise in trajectory data. Our experiments show that our approach is highly accurate in distinguishing similar and dissimilar portions as compared to earlier methods even with sparse sampling

Unsupervised Video Understanding by Reconciliation of Posture Similarities

Understanding human activity and being able to explain it in detail surpasses mere action classification by far in both complexity and value. The challenge is thus to describe an activity on the basis of its most fundamental constituents, the individual postures and their distinctive transitions. Supervised learning of such a fine-grained representation based on elementary poses is very tedious and does not scale. Therefore, we propose a completely unsupervised deep learning procedure based solely on video sequences, which starts from scratch without requiring pre-trained networks, predefined body models, or keypoints. A combinatorial sequence matching algorithm proposes relations between frames from subsets of the training data, while a CNN is reconciling the transitivity conflicts of the different subsets to learn a single concerted pose embedding despite changes in appearance across sequences. Without any manual annotation, the model learns a structured representation of postures and their temporal development. The model not only enables retrieval of similar postures but also temporal super-resolution. Additionally, based on a recurrent formulation, next frames can be synthesized.Comment: Accepted by ICCV 201

Computational Modeling Approaches For Task Analysis In Robotic-Assisted Surgery

Surgery is continuously subject to technological innovations including the introduction of robotic surgical devices. The ultimate goal is to program the surgical robot to perform certain difficult or complex surgical tasks in an autonomous manner. The feasibility of current robotic surgery systems to record quantitative motion and video data motivates developing descriptive mathematical models to recognize, classify and analyze surgical tasks. Recent advances in machine learning research for uncovering concealed patterns in huge data sets, like kinematic and video data, offer a possibility to better understand surgical procedures from a system point of view. This dissertation focuses on bridging the gap between these two lines of the research by developing computational models for task analysis in robotic-assisted surgery. The key step for advance study in robotic-assisted surgery and autonomous skill assessment is to develop techniques that are capable of recognizing fundamental surgical tasks intelligently. Surgical tasks and at a more granular level, surgical gestures, need to be quantified to make them amenable for further study. To answer to this query, we introduce a new framework, namely DTW-kNN, to recognize and classify three important surgical tasks including suturing, needle passing and knot tying based on kinematic data captured using da Vinci robotic surgery system. Our proposed method needs minimum preprocessing that results in simple, straightforward and accurate framework which can be applied for any autonomous control system. We also propose an unsupervised gesture segmentation and recognition (UGSR) method which has the ability to automatically segment and recognize temporal sequence of gestures in RMIS task. We also extent our model by applying soft boundary segmentation (Soft-UGSR) to address some of the challenges that exist in the surgical motion segmentation. The proposed algorithm can effectively model gradual transitions between surgical activities. Additionally, surgical training is undergoing a paradigm shift with more emphasis on the development of technical skills earlier in training. Thus metrics for the skills, especially objective metrics, become crucial. One field of surgery where such techniques can be developed is robotic surgery, as here all movements are already digitalized and therefore easily susceptible to analysis. Robotic surgery requires surgeons to perform a much longer and difficult training process which create numerous new challenges for surgical training. Hence, a new method of surgical skill assessment is required to ensure that surgeons have adequate skill level to be allowed to operate freely on patients. Among many possible approaches, those that provide noninvasive monitoring of expert surgeon and have the ability to automatically evaluate surgeon\u27s skill are of increased interest. Therefore, in this dissertation we develop a predictive framework for surgical skill assessment to automatically evaluate performance of surgeon in RMIS. Our classification framework is based on the Global Movement Features (GMFs) which extracted from kinematic movement data. The proposed method addresses some of the limitations in previous work and gives more insight about underlying patterns of surgical skill levels