A deep learning approach towards railway safety risk assessment

Railway stations are essential aspects of railway systems, and they play a vital role in public daily life. Various types of AI technology have been utilised in many fields to ensure the safety of people and their assets. In this paper, we propose a novel framework that uses computer vision and pattern recognition to perform risk management in railway systems in which a convolutional neural network (CNN) is applied as a supervised machine learning model to identify risks. However, risk management in railway stations is challenging because stations feature dynamic and complex conditions. Despite extensive efforts by industry associations and researchers to reduce the number of accidents and injuries in this field, such incidents still occur. The proposed model offers a beneficial method for obtaining more accurate motion data, and it detects adverse conditions as soon as possible by capturing fall, slip and trip (FST) events in the stations that represent high-risk outcomes. The framework of the presented method is generalisable to a wide range of locations and to additional types of risks

Recent advances in video analytics for rail network surveillance for security, trespass and suicide prevention— a survey

Railway networks systems are by design open and accessible to people, but this presents challenges in the prevention of events such as terrorism, trespass, and suicide fatalities. With the rapid advancement of machine learning, numerous computer vision methods have been developed in closed-circuit television (CCTV) surveillance systems for the purposes of managing public spaces. These methods are built based on multiple types of sensors and are designed to automatically detect static objects and unexpected events, monitor people, and prevent potential dangers. This survey focuses on recently developed CCTV surveillance methods for rail networks, discusses the challenges they face, their advantages and disadvantages and a vision for future railway surveillance systems. State-of-the-art methods for object detection and behaviour recognition applied to rail network surveillance systems are introduced, and the ethics of handling personal data and the use of automated systems are also considered

Real-World Anomaly Detection in Video Using Spatio-Temporal Features Analysis for Weakly Labelled Data with Auto Label Generation

Detecting anomalies in videos is a complex task due to diverse content, noisy labeling, and a lack of frame-level labeling. To address these challenges in weakly labeled datasets, we propose a novel custom loss function in conjunction with the multi-instance learning (MIL) algorithm. Our approach utilizes the UCF Crime and ShanghaiTech datasets for anomaly detection. The UCF Crime dataset includes labeled videos depicting a range of incidents such as explosions, assaults, and burglaries, while the ShanghaiTech dataset is one of the largest anomaly datasets, with over 400 video clips featuring three different scenes and 130 abnormal events. We generated pseudo labels for videos using the MIL technique to detect frame-level anomalies from video-level annotations, and to train the network to distinguish between normal and abnormal classes. We conducted extensive experiments on the UCF Crime dataset using C3D and I3D features to test our model\u27s performance. For the ShanghaiTech dataset, we used I3D features for training and testing. Our results show that with I3D features, we achieve an 84.6% frame-level AUC score for the UCF Crime dataset and a 92.27% frame-level AUC score for the ShanghaiTech dataset, which are comparable to other methods used for similar datasets

Deep-Facial Feature-Based Person Reidentification for Authentication in Surveillance Applications

Person reidentification (Re-ID) has been a problem recently faced in computer vision. Most of the existing methods focus on body features which are captured in the scene with high-end surveillance system. However, it is unhelpful for authentication. The technology came up empty in surveillance scenario such as in London’s subway bomb blast, and Bangalore ATM brutal attack cases, even though the suspected images exist in official databases. Hence, the prime objective of this chapter is to develop an efficient facial feature-based person reidentification framework for controlled scenario to authenticate a person. Initially, faces are detected by faster region-based convolutional neural network (Faster R-CNN). Subsequently, landmark points are obtained using supervised descent method (SDM) algorithm, and the face is recognized, by the joint Bayesian model. Each image is given an ID in the training database. Based on their similarity with the query image, it is ranked with the Re-ID index. The proposed framework overcomes the challenges such as pose variations, low resolution, and partial occlusions (mask and goggles). The experimental results (accuracy) on benchmark dataset demonstrate the effectiveness of the proposed method which is inferred from the observation of receiver operating characteristic (ROC) curve and cumulative matching characteristics (CMC) curve

A spatio-temporal learning approach for crowd activity modelling to detect anomalies

With security and surveillance gaining paramount importance in recent years, it has become important to reliably automate some surveillance tasks for monitoring crowded areas. The need to automate this process also supports human operators who are overwhelmed with a large number of security screens to monitor. Crowd events like excess usage throughout the day, sudden peaks in crowd volume, chaotic motion (obvious to spot) all emerge over time which requires constant monitoring in order to be informed of the event build up. To ease this task, the computer vision community has been addressing some surveillance tasks using image processing and machine learning techniques. Currently tasks such as crowd density estimation or people counting, crowd detection and abnormal crowd event detection are being addressed. Most of the work has focused on crowd detection and estimation with the focus slowly shifting on crowd event learning for abnormality detection.This thesis addresses crowd abnormality detection. However, by way of the modelling approach used, implicitly, the tasks of crowd detection and estimation are also handled. The existing approaches in the literature have a number of drawbacks that keep them from being scalable for any public scene. Most pieces of work use simple scene settings where motion occurs wholly in the near-field or far-field of the camera view. Thus, with assumptions on the expected location of person motion, small blobs are arbitrarily filtered out as noise when they may be legitimate motion in the far-field. Such an approach makes it difficult to deal with complex scenes where entry/exit points occur in the centre of the scene or multiple pathways running from the near to the far-field of the camera view that produce blobs of differing sizes. Further, most authors assume the number of directions people motion should exhibit rather than discover what these may be. Approaches with such assumptions would result in loss of accuracy while dealing with (say) a railway platform which shows a number of motion directions, namely two-way, one-way, dispersive, etc. Finally, very few contributions of work use time as a video feature to model the human intuitiveness of time-of-day abnormalities. That is certain motion patterns may be abnormal if they have not been seen for a given time of day. Most works use it (time) as an extra qualifier to spatial data for trajectory definition.In this thesis most of these drawbacks have been addressed by dealing with these in the modelling of crowd activity. Firstly, no assumptions are made on scene structure or blob sizes resulting therefrom. The optical flow algorithm used is robust and even the noise presented (which is infact unwanted motion of swaying hands and legs as opposed to that from the torso) is fairly consistent and therefore can be factored into the modelling. Blobs, no matter what the size are not discarded as they may be legitimate emerging motion in the far-field. The modelling also deals with paths extending from the far to the near-field of the camera view and segments these such that each segment contains self-comparable fields of motion. The need for a normalisation factor for comparisons across near and far field motion fields implies prior knowledge of the scene. As the system is intended for generic public locations having varying scene structures, normalisation is not an option in the processing used and yet the near & far-field motion changes are accounted for. Secondly, this thesis describes a system that learns the true distribution of motion along the detected paths and maintains these. The approach is such that doing so does not generalise the direction distributions which would cause loss in precision. No impositions are made on expected motion and if the underlying motion is well defined (one-way or two-way), then this is represented as a well defined distribution and as a mixture of directions if the underlying motion presents itself as so.Finally, time as a video feature is used to allow for activity to re-enforce itself on a daily basis such that motion patterns for a given time and space begin to define themselves through re-enforcement which acts as the model used for abnormality detection in time and space (spatio-temporal). The system has been tested with real-world data datasets with varying fields of camera view. The testing has shown no false negatives, very few false positives and detects crowd abnormalities quite well with respect to the ground truths of the datasets used

Detection of abandoned objects in crowded environments

With concerns about terrorism and global security on the rise, it has become vital to have in place efficient threat detection systems that will identify potentially dangerous situations, and alert the authorities to take appropriate action. Of particular relevance is the case of abandoned objects in highly crowded areas. This thesis describes a general framework that recognizes the event of someone leaving an object unattended in forbidden areas. Our approach involves the recognition of four sub-events that characterize the activity of interest. When an unaccompanied object is found, the system analyzes its history to determine its most likely owner(s). Through subsequent frames, the system keeps a lookout for the owner, whose presence in or disappearance from the scene defines the status of the object and determines the appropriate course of action. The system was successfully implemented and tested on several standardized datasets.Electrical and Computer Engineerin

Stopping the Traffic: the National Vigilance Association and the international fight against the ‘white slave’ trade (1899–c.1909)

The National Vigilance Association was the most prominent organization to take on the fight against sex trafficking in turn-of-the-century Britain. In 1899, it established and presided over the first global multidenominational anti-trafficking task force, the International Bureau for the Suppression of the White Slave Traffic (later Traffic in Persons). This article focuses on the configuration of the National Vigilance Association's anti-trafficking work during the formative years of the Bureau, paying particular attention to the relationship between the Association and the state. It sheds new light on the nature and significance of both the Association's role in the Bureau and the Association's domestic anti-trafficking operations. It exposes the way in which, while making notable advancements in the fight against trafficking, the Association brought an assumption of British superiority to its international work, and operated on the basis of a misdiagnosis of ‘sexual exploitation’ informed by a gender- and class-biased xenophobia, such as to detract from its commitment to the suppression of trafficking