Bayesian nonparametric approaches to abnormality detection in video surveillance

In data science, anomaly detection is the process of identifying the items, events or observations which do not conform to expected patterns in a dataset. As widely acknowledged in the computer vision community and security management, discovering suspicious events is the key issue for abnormal detection in video surveil-lance. The important steps in identifying such events include stream data segmentation and hidden patterns discovery. However, the crucial challenge in stream data segmenta-tion and hidden patterns discovery are the number of coherent segments in surveillance stream and the number of traffic patterns are unknown and hard to specify. Therefore, in this paper we revisit the abnormality detection problem through the lens of Bayesian nonparametric (BNP) and develop a novel usage of BNP methods for this problem. In particular, we employ the Infinite Hidden Markov Model and Bayesian Nonparamet-ric Factor Analysis for stream data segmentation and pattern discovery. In addition, we introduce an interactive system allowing users to inspect and browse suspicious events.

Fast vision-based catheter 3D reconstruction

Continuum robots offer better maneuverability and inherent compliance and are well-suited for surgical applications as catheters, where gentle interaction with the environment is desired. However, sensing their shape and tip position is a challenge as traditional sensors can not be employed in the way they are in rigid robotic manipulators. In this paper, a high speed vision-based shape sensing algorithm for real-time 3D reconstruction of continuum robots based on the views of two arbitrary positioned cameras is presented. The algorithm is based on the closed-form analytical solution of the reconstruction of quadratic curves in 3D space from two arbitrary perspective projections. High-speed image processing algorithms are developed for the segmentation and feature extraction from the images. The proposed algorithms are experimentally validated for accuracy by measuring the tip position, length and bending and orientation angles for known circular and elliptical catheter shaped tubes. Sensitivity analysis is also carried out to evaluate the robustness of the algorithm. Experimental results demonstrate good accuracy (maximum errors of  ±0.6 mm and  ±0.5 deg), performance (200 Hz), and robustness (maximum absolute error of 1.74 mm, 3.64 deg for the added noises) of the proposed high speed algorithms.

Segmentation of special event attendees using personal values: relationships with satisfaction and behavioural intentions

This article presents the findings from research undertaken within a conceptual framework that included personal values, satisfaction and post-consumption behavioural intentions. The findings of a quantitative study (n = 354) conducted at a theatre-event indicate that attendees who were more inclined to place importance on their 'connectedness' with others were generally more satisfied with their attendance overall and with most of the attributes of the special event that were measured. Similar results were also found for attendees' post-con-sumption behavioural intentions; however, other personal value systems, such as that associated with hedonism, also emerged as important. These results can be used by managers and marketers of special events to enhance the special event experience and contribute to the industry's sustainability.

Linking attitudes and demographics in a tourist segmentation model : a two-stage approach

Segmentation has been widely studied in tourism research e.g. Dolnicar (2004). Dawley (2006) points that commonly used segmentation variables such as demographics lead to identifiable segments which are not actionable while other useful approaches e.g. psychographics, are actionable but not identifiable. The objective of this paper is to develop a two-stage linkage approach to segmentation whereby cluster analysis using psychographic variables is conducted within demographic group. Demographic groups are selected based on propensity to travel. This research utilizes data generated from a cross-sectional self-completed survey of 49,105 Australian respondents on travel and tourism. The managerial usefulness of this segmentation is assessed. Clearly segments can be directly linked both demographically and psychographically.

Psychographic segmentation of Beijing adult population and food consumption

Responses from a large (801) random sample of Beijing’s adult population were used to carry out this "values and lifestyles" segmentation process and it measured consumers’ "values" and "lifestyles" directly. The results indicate that "values and lifestyle" segmentation provides marketers with a more comprehensive understanding of the consumers than by demographics alone. This study also demonstrates that marketers should not carry out segmentation automatically. They need to determine where consumers perceive a particular category of product on the "luxury" and "non-luxury" continuum before deciding whether to carry out the segmentation process or not.

Robust recognition and segmentation of human actions using HMMs with missing observations

This paper describes the integration of missing observation data with hidden Markov models to create a framework that is able to segment and classify individual actions from a stream of human motion using an incomplete 3D human pose estimation. Based on this framework, a model is trained to automatically segment and classify an activity sequence into its constituent subactions during inferencing. This is achieved by introducing action labels into the observation vector and setting these labels as missing data during inferencing, thus forcing the system to infer the probability of each action label. Additionally, missing data provides recognition-level support for occlusions and imperfect silhouette segmentation, permitting the use of a fast (real-time) pose estimation that delegates the burden of handling undetected limbs onto the action recognition system. Findings show that the use of missing data to segment activities is an accurate and elegant approach. Furthermore, action recognition can be accurate even when almost half of the pose feature data is missing due to occlusions, since not all of the pose data is important all of the time.

Improved fade and dissolve detection for reliable video segmentation

We present improved algorithms for automatic fade and dissolve detection in digital video analysis. We devise new two-step algorithms for fade and dissolve detection and introduce a method for eliminating false positives from a list of detected candidate transitions. In our detailed study of these gradual shot transitions, our objective has been to accurately classify the type of transitions (fade-in, fade-out, and dissolve) and to precisely locate the boundary of the transitions. This distinguishes our work from early work in scene change detection which focuses on identifying the existence of a transition rather than its precise temporal extent. We evaluate our algorithms against two other commonly used methods on a comprehensive data set, and demonstrate the improved performance due to our enhancements.

New enhancements to cut, fade, and dissolve detection processes in video segmentation

We present improved algorithms for cut, fade, and dissolve detection which are fundamental steps in digital video analysis. In particular, we propose a new adaptive threshold determination method that is shown to reduce artifacts created by noise and motion in scene cut detection. We also describe new two-step algorithms for fade and dissolve detection, and introduce a method for eliminating false positives from a list of detected candidate transitions. In our detailed study of these gradual shot transitions, our objective has been to accurately classify the type of transitions (fade-in, fade-out, and dissolve) and to precisely locate the boundary of the transitions. This distinguishes our work from other early work in scene change detection which tends to focus primarily on identifying the existence of a transition rather than its precise temporal extent. We evaluate our improved algorithms against two other commonly used shot detection techniques on a comprehensive data set, and demonstrate the improved performance due to our enhancements.

Human activity learning and segmentation using partially hidden discriminative models

Learning and understanding the typical patterns in the daily activities and routines of people from low-level sensory data is an important problem in many application domains such as building smart environments, or providing intelligent assistance. Traditional approaches to this problem typically rely on supervised learning and generative models such as the hidden Markov models and its extensions. While activity data can be readily acquired from pervasive sensors, e.g. in smart environments, providing manual labels to support supervised training is often extremely expensive. In this paper, we propose a new approach based on semi-supervised training of partially hidden discriminative models such as the conditional random field (CRF) and the maximum entropy Markov model (MEMM). We show that these models allow us to incorporate both labeled and unlabeled data for learning, and at the same time, provide us with the flexibility and accuracy of the discriminative framework. Our experimental results in the video surveillance domain illustrate that these models can perform better than their generative counterpart, the partially hidden Markov model, even when a substantial amount of labels are unavailable.

Fuzzy clustering of color and texture features for image segmentation: a study on satellite image retrieval

In this paper, a new image segmentation approach that integrates color and texture features using the fuzzy c-means clustering algorithm is described. To demonstrate the applicability of the proposed approach to satellite image retrieval, an interactive region-based image query system is designed and developed. A database comprising 400 multispectral satellite images is used to evaluate the performance of the system. The results are analyzed and discussed, and a performance comparison with other methods is included. The outcomes reveal that the proposed approach is able to improve the quality of the segmentation results as well as the retrieval performance.

Hybrid image segmentation using fuzzy clustering algorithms satellite imagery searching and retrieval

Satellite image processing is a complex task that has received considerable attention from many researchers. In this paper, an interactive image query system for satellite imagery searching and retrieval is proposed. Like most image retrieval systems, extraction of image features is the most important step that has a great impact on the retrieval performance. Thus, a new technique that fuses color and texture features for segmentation is introduced. Applicability of the proposed technique is assessed using a database containing multispectral satellite imagery. The experiments demonstrate that the proposed segmentation technique is able to improve quality of the segmentation results as well as the retrieval performance.