Textures of optical flow for real-time anomaly detection in crowds

Automated visual surveillance of crowds is a rapidly growing area of research. In this paper we focus on motion representation for the purpose of abnormality detection in crowded scenes. We propose a novel visual representation called textures of optical flow. The proposed representation measures the uniformity of a flow field in order to detect anomalous objects such as bicycles, vehicles and skateboarders; and can be combined with spatial information to detect other forms of abnormality. We demonstrate that the proposed approach outperforms state-of-the-art anomaly detection algorithms on a large, publicly-available dataset.

Radiographer commenting of trauma radiographs : a survey of the benefits, barriers and enablers to participation in an Australian healthcare setting

Introduction Radiographer abnormality detection systems that highlight abnormalities on trauma radiographs (‘red dot’ system) have been operating for more than 30 years. Recently, a number of pitfalls have been identified. These limitations initiated the evolution of a radiographer commenting system, whereby a radiographer provides a brief description of abnormalities identified in emergency healthcare settings. This study investigated radiographers' participation in abnormality detection systems, their perceptions of benefits, barriers and enablers to radiographer commenting, and perceptions of potential radiographer image interpretation services for emergency settings. Methods A cross-sectional survey was implemented. Participants included radiographers from four metropolitan hospitals in Queensland, Australia. Conventional descriptive statistics, histograms and thematic analysis were undertaken. Results Seventy-three surveys were completed and included in the analysis (68% response rate); 30 (41%) of respondents reported participating in abnormality detection in 20% or less of examinations, and 26(36%) reported participating in 80% or more of examinations. Five overarching perceived benefits of radiographer commenting were identified: assisting multidisciplinary teams, patient care, radiographer ability, professional benefits and quality of imaging. Frequently reported perceived barriers included ‘difficulty accessing image interpretation education’, ‘lack of time’ and ‘low confidence in interpreting radiographs’. Perceived enablers included ‘access to image interpretation education’ and ‘support from radiologist colleagues’. Conclusions A range of factors are likely to contribute to the successful implementation of radiographer commenting in addition to abnormality detection in emergency settings. Effective image interpretation education amenable to completion by radiographers would likely prove valuable in preparing radiographers for participation in abnormality detection and commenting systems in emergency settings.

Dynamic texture reconstruction from sparse codes for unusual event detection in crowded scenes

Unusual event detection in crowded scenes remains challenging because of the diversity of events and noise. In this paper, we present a novel approach for unusual event detection via sparse reconstruction of dynamic textures over an overcomplete basis set, with the dynamic texture described by local binary patterns from three orthogonal planes (LBPTOP). The overcomplete basis set is learnt from the training data where only the normal items observed. In the detection process, given a new observation, we compute the sparse coefficients using the Dantzig Selector algorithm which was proposed in the literature of compressed sensing. Then the reconstruction errors are computed, based on which we detect the abnormal items. Our application can be used to detect both local and global abnormal events. We evaluate our algorithm on UCSD Abnormality Datasets for local anomaly detection, which is shown to outperform current state-of-the-art approaches, and we also get promising results for rapid escape detection using the PETS2009 dataset.

An MRF based abnormal event detection approach using motion and appearance features

Abnormal event detection has attracted a lot of attention in the computer vision research community during recent years due to the increased focus on automated surveillance systems to improve security in public places. Due to the scarcity of training data and the definition of an abnormality being dependent on context, abnormal event detection is generally formulated as a data-driven approach where activities are modeled in an unsupervised fashion during the training phase. In this work, we use a Gaussian mixture model (GMM) to cluster the activities during the training phase, and propose a Gaussian mixture model based Markov random field (GMM-MRF) to estimate the likelihood scores of new videos in the testing phase. Further-more, we propose two new features: optical acceleration, and the histogram of optical flow gradients; to detect the presence of any abnormal objects and speed violations in the scene. We show that our proposed method outperforms other state of the art abnormal event detection algorithms on publicly available UCSD dataset.

Fault detection of wind turbine drivetrain utilizing power-speed characteristics

Wind energy, being the fastest growing renewable energy source in the present world, requires a large number of wind turbines to transform wind energy into electricity. One factor driving the cost of this energy is the reliable operation of these turbines. Therefore, it is a growing requirement within the wind farm community, to monitor the operation of the wind turbines on a continuous basis so that a possible fault can be detected ahead of time. As the wind turbine operates in an environment of constantly changing wind speed, it is a challenging task to design a fault detection technique which can accommodate the stochastic operational behavior of the turbines. Addressing this issue, this paper proposes a novel fault detection criterion which is robust against operational uncertainty, as well as having the ability to quantify severity level specifically of the drivetrain abnormality within an operating wind turbine. A benchmark model of wind turbine has been utilized to simulate drivetrain fault condition and effectiveness of the proposed technique has been tested accordingly. From the simulation result it can be concluded that the proposed criterion exhibits consistent performance for drivetrain faults for varying wind speed and has linear relationship with the fault severity level.