Transportation, Terrorism and Crime: Deterrence, Disruption and Resilience

Abstract: Terrorists likely have adopted vehicle ramming as a tactic because it can be carried out by an individual (or “lone wolf terrorist”), and because the skills required are minimal (e.g. the ability to drive a car and determine locations for creating maximum carnage). Studies of terrorist activities against transportation assets have been conducted to help law enforcement agencies prepare their communities, create mitigation measures, conduct effective surveillance and respond quickly to attacks. This study reviews current research on terrorist tactics against transportation assets, with an emphasis on vehicle ramming attacks. It evaluates some of the current attack strategies, and the possible mitigation or response tactics that may be effective in deterring attacks or saving lives in the event of an attack. It includes case studies that can be used as educational tools for understanding terrorist methodologies, as well as ordinary emergencies that might become a terrorist’s blueprint

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

The detection of concealed firearm carrying trough CCTV: the role of affect recognition

This research aimed to explore whether the recognition of offenders with a concealed firearm by a human operator might be based on the recognition of affective (negative) state derived from non-verbal behaviour that is accessible from CCTV images. Since a firearm is concealed, it has been assumed that human observers would respond to subtle cues which individuals inherently produce whilst carrying a hidden firearm. These cues are believed to be reflected in the body language of those carrying firearms and might be apprehended by observers at a conscious or subconscious level. Another hypothesis is that the ability to recognize the carrier of concealed firearm in the CCTV footage might be affected by other factors, such as the skills in decoding an affective state of others and the viewpoint of observation of the surveillance targets. In order to give a theoretical and experimental basis for these hypotheses the first objective was to examine the extant literature to determine what is known about recognition of affect from non-verbal cues (e.g. facial expressions and body movement), and how it can be applied to the detection of human mal-intent. A second objective was to explore this subject in relation to the detection of concealed firearm carrying through performing a number of experimental studies. The studies employed experts, i.e. CCTV operators and mainly the lay people as participants. Also, various experimental techniques such as questionnaires and eye-tracking registration were used to investigate the topic. The results show that human observers seem to use visual indicators of affective state of surveillance targets to make a decision whether or not the individuals are carrying a concealed firearm. The most prominent cues were face, and upper body of surveillance targets, gait, posture and arm movements. The test of decoding ability did not show sufficient relationship with the ability to detect a concealed firearm bearer. The performance on the task might be view dependent. Further research into this topic will be needed to generate strategies that would support reliable detection of concealed firearm carrying through employing of related affective behavioural cues

Automated Knowledge Generation with Persistent Surveillance Video

The Air Force has increasingly invested in persistent surveillance platforms gathering a large amount of surveillance video. Ordinarily, intelligence analysts watch the video to determine if suspicious activities are occurring. This approach to video analysis can be a very time and manpower intensive process. Instead, this thesis proposes that by using tracks generated from persistent video, we can build a model to detect events for an intelligence analyst. The event that we chose to detect was a suspicious surveillance activity known as a casing event. To test our model we used Global Positioning System (GPS) tracks generated from vehicles driving in an urban area. The results show that over 400 vehicles can be monitored simultaneously in real-time and casing events are detected with high probability (43 of 43 events detected with only 4 false positives). Casing event detections are augmented by determining which buildings are being targeted. In addition, persistent surveillance video is used to construct a social network from vehicle tracks based on the interactions of those tracks. Social networks that are constructed give us further information about the suspicious actors flagged by the casing event detector by telling us who the suspicious actor has interacted with and what buildings they have visited. The end result is a process that automatically generates information from persistent surveillance video providing additional knowledge and understanding to intelligence analysts about terrorist activities

Outside the fence,\u27 the threat to the U.S. Aviation Industry

The purpose of this thesis was to examine the threat to the Aviation Industry from within the United States. The overall investigation starts with one assumption “that there will at some time in the near future be a covert operative group that desires to attack or engage the United States in war on its home land.” The principles of war will be analyzed resulting in covert cell guidance specifically; “economy of force” will require the covert units to be as small as possible to affect as many nodes as possible. Endurance will require the covert team to restrict any tactics that would be high risk, and would prohibit the use of suicide tactics. There has also been a redefinition of warfare in the last several years. What has emerged is a form of unrestricted warfare. The covert cell may abide by the principles of war while engaging the U.S. in unrestricted warfare. These assumptions lead to a center of gravity determination and terrorism as the possible action for the desired effect. Attack and weapon selection analysis results in the selection of the 50 caliber sniper rifle with armor-piercing incendiary ammunition as the most probable attack tactic executed against urban airport environments. Possible solution analysis of acoustic, mid wave infrared and optical augmentation systems reveals the advantages of each of these approaches. The conclusion is that open system architecture should be used to tailor the sensor suite around each airport based on the vital area locations with respect to the urban layout and the best sniping positions. This will lead to a multi- layer and multi-system defensive posture around each airport significantly reducing the risk of a drawn out terror campaign which involves the airline industry

Recent Trends in Computational Intelligence

Traditional models struggle to cope with complexity, noise, and the existence of a changing environment, while Computational Intelligence (CI) offers solutions to complicated problems as well as reverse problems. The main feature of CI is adaptability, spanning the fields of machine learning and computational neuroscience. CI also comprises biologically-inspired technologies such as the intellect of swarm as part of evolutionary computation and encompassing wider areas such as image processing, data collection, and natural language processing. This book aims to discuss the usage of CI for optimal solving of various applications proving its wide reach and relevance. Bounding of optimization methods and data mining strategies make a strong and reliable prediction tool for handling real-life applications

Recognition, Analysis, and Assessments of Human Skills using Wearable Sensors

One of the biggest social issues in mature societies such as Europe and Japan is the aging population and declining birth rate. These societies have a serious problem with the retirement of the expert workers, doctors, and engineers etc. Especially in the sectors that require long time to make experts in fields like medicine and industry; the retirement and injuries of the experts, is a serious problem. The technology to support the training and assessment of skilled workers (like doctors, manufacturing workers) is strongly required for the society. Although there are some solutions for this problem, most of them are video-based which violates the privacy of the subjects. Furthermore, they are not easy to deploy due to the need for large training data. This thesis provides a novel framework to recognize, analyze, and assess human skills with minimum customization cost. The presented framework tackles this problem in two different domains, industrial setup and medical operations of catheter-based cardiovascular interventions (CBCVI). In particular, the contributions of this thesis are four-fold. First, it proposes an easy-to-deploy framework for human activity recognition based on zero-shot learning approach, which is based on learning basic actions and objects. The model recognizes unseen activities by combinations of basic actions learned in a preliminary way and involved objects. Therefore, it is completely configurable by the user and can be used to detect completely new activities. Second, a novel gaze-estimation model for attention driven object detection task is presented. The key features of the model are: (i) usage of the deformable convolutional layers to better incorporate spatial dependencies of different shapes of objects and backgrounds, (ii) formulation of the gaze-estimation problem in two different way, as a classification as well as a regression problem. We combine both formulations using a joint loss that incorporates both the cross-entropy as well as the mean-squared error in order to train our model. This enhanced the accuracy of the model from 6.8 by using only the cross-entropy loss to 6.4 for the joint loss. The third contribution of this thesis targets the area of quantification of quality of i actions using wearable sensor. To address the variety of scenarios, we have targeted two possibilities: a) both expert and novice data is available , b) only expert data is available, a quite common case in safety critical scenarios. Both of the developed methods from these scenarios are deep learning based. In the first one, we use autoencoders with OneClass SVM, and in the second one we use the Siamese Networks. These methods allow us to encode the expert’s expertise and to learn the differences between novice and expert workers. This enables quantification of the performance of the novice in comparison to the expert worker. The fourth contribution, explicitly targets medical practitioners and provides a methodology for novel gaze-based temporal spatial analysis of CBCVI data. The developed methodology allows continuous registration and analysis of gaze data for analysis of the visual X-ray image processing (XRIP) strategies of expert operators in live-cases scenarios and may assist in transferring experts’ reading skills to novices

A Wide Area Multiview Static Crowd Estimation System Using UAV and 3D Training Simulator

Crowd size estimation is a challenging problem, especially when the crowd is spread over a significant geographical area. It has applications in monitoring of rallies and demonstrations and in calculating the assistance requirements in humanitarian disasters. Therefore, accomplishing a crowd surveillance system for large crowds constitutes a significant issue. UAV-based techniques are an appealing choice for crowd estimation over a large region, but they present a variety of interesting challenges, such as integrating per-frame estimates through a video without counting individuals twice. Large quantities of annotated training data are required to design, train, and test such a system. In this paper, we have first reviewed several crowd estimation techniques, existing crowd simulators and data sets available for crowd analysis. Later, we have described a simulation system to provide such data, avoiding the need for tedious and error-prone manual annotation. Then, we have evaluated synthetic video from the simulator using various existing single-frame crowd estimation techniques. Our findings show that the simulated data can be used to train and test crowd estimation, thereby providing a suitable platform to develop such techniques. We also propose an automated UAV-based 3D crowd estimation system that can be used for approximately static or slow-moving crowds, such as public events, political rallies, and natural or man-made disasters. We evaluate the results by applying our new framework to a variety of scenarios with varying crowd sizes. The proposed system gives promising results using widely accepted metrics including MAE, RMSE, Precision, Recall, and F1 score to validate the results

The Challenge of Protecting Transit and Passenger Rail: Understanding How Security Works Against Terrorism

Terrorists see transit and passenger rail as an attractive target. Designed for public convenience, trains and stations offer terrorists easy access to crowds of people in confined environments where there are minimal security risks and attacks can cause high casualties. This report examines the unique attributes of the terrorist threat, how security measures against terrorism have evolved over the years, and their overall effectiveness. Does security work? Empirical evidence is hard to come by. Terrorist incidents are statistically rare and random, making it difficult to discern effects. The fact that terrorists focus most of their attacks on targets with little or no security suggests that security influences their choice of targets. Increased security does not reduce terrorism overall, but appears to push terrorists toward softer targets. These indirect effects are visible only over long periods of time. Public surface transportation poses unique challenges. It is not easy to increase security without causing inconvenience, unreasonably slowing travel times, adding significant costs, and creating vulnerable queues of people waiting to pass through security checkpoints. This has compelled rail operators to explore other options: enlisting passengers and staff in alerting authorities to suspicious objects or behavior, random passenger screening, designing new stations to facilitate surveillance and reduce potential casualties from explosions or fire, and ensuring rapid intervention