Traffic Danger Recognition With Surveillance Cameras Without Training Data

We propose a traffic danger recognition model that works with arbitrary traffic surveillance cameras to identify and predict car crashes. There are too many cameras to monitor manually. Therefore, we developed a model to predict and identify car crashes from surveillance cameras based on a 3D reconstruction of the road plane and prediction of trajectories. For normal traffic, it supports real-time proactive safety checks of speeds and distances between vehicles to provide insights about possible high-risk areas. We achieve good prediction and recognition of car crashes without using any labeled training data of crashes. Experiments on the BrnoCompSpeed dataset show that our model can accurately monitor the road, with mean errors of 1.80% for distance measurement, 2.77 km/h for speed measurement, 0.24 m for car position prediction, and 2.53 km/h for speed prediction.Comment: To be published in proceedings of Advanced Video and Signal-based Surveillance (AVSS), 2018 15th IEEE International Conference on, pp. 378-383, IEE

Detect the unexpected: a science for surveillance

Purpose – The purpose of this paper is to outline a strategy for research development focused on addressing the neglected role of visual perception in real life tasks such as policing surveillance and command and control settings. Approach – The scale of surveillance task in modern control room is expanding as technology increases input capacity at an accelerating rate. The authors review recent literature highlighting the difficulties that apply to modern surveillance and give examples of how poor detection of the unexpected can be, and how surprising this deficit can be. Perceptual phenomena such as change blindness are linked to the perceptual processes undertaken by law-enforcement personnel. Findings – A scientific programme is outlined for how detection deficits can best be addressed in the context of a multidisciplinary collaborative agenda between researchers and practitioners. The development of a cognitive research field specifically examining the occurrence of perceptual “failures” provides an opportunity for policing agencies to relate laboratory findings in psychology to their own fields of day-to-day enquiry. Originality/value – The paper shows, with examples, where interdisciplinary research may best be focussed on evaluating practical solutions and on generating useable guidelines on procedure and practice. It also argues that these processes should be investigated in real and simulated context-specific studies to confirm the validity of the findings in these new applied scenarios

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

Leveraging Traffic and Surveillance Video Cameras for Urban Traffic

The objective of this project was to investigate the use of existing video resources, such as traffic cameras, police cameras, red light cameras, and security cameras for the long-term, real-time collection of traffic statistics. An additional objective was to gather similar statistics for pedestrians and bicyclists. Throughout the course of the project, we investigated several methods for tracking vehicles under challenging conditions. The initial plan called for tracking based on optical flow. However, it was found that current optical flow–estimating algorithms are not well suited to low-quality video—hence, developing optical flow methods for low-quality video has been one aspect of this project. The method eventually used combines basic optical flow tracking with a learning detector for each tracked object—that is, the object is tracked both by its apparent movement and by its appearance should it temporarily disappear from or be obscured in the frame. We have produced a prototype software that allows the user to specify the vehicle trajectories of interest by drawing their shapes superimposed on a video frame. The software then tracks each vehicle as it travels through the frame, matches the vehicle’s movements to the most closely matching trajectory, and increases the vehicle count for that trajectory. In terms of pedestrian and bicycle counting, the system is capable of tracking these “objects” as well, though at present it is not capable of distinguishing between the three classes automatically. Continuing research by the principal investigator under a different grant will establish this capability as well.Illinois Department of Transportation, R27-131Ope