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

Driving offences

Copyright @ 2010, Taylor & Francis Group. This material is posted on this site with the permission of the publishers.This chapter on driving offences will largely follow the template of earlier chapters except that owing to their vast number, a limited selection only will be examined based on their high volume, seriousness and public concern. The first section will define what driving offences are, how they developed alongside the emerging car culture, and it will consider the contemporary landscape. The second section will give a general overview of patterns and trends, those most likely to engage in road traffic offending, and kinds of explanations voiced by drivers and theoretical approaches used. The next three sections will follow a similar pattern and focus on speeding, bad driving – mostly dangerous and careless offences, and impaired driving – mostly drink-driving but mentioning drug-driving and fatigued driving. In addition, contemporary debates and key issues concerning each will be considered, along with official responses to each offence category comprising court-based penalties and other measures. The final section will draw the key threads and themes together, noting the danger of work-related driving. Given that up to a third of all road traffic collisions involve somebody at work at the time accounting for up to 20 fatalities and 250 serious injuries every week (DfT and HSE, 2003), the importance of reducing traffic offending is clear

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

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

Scene Monitoring With A Forest Of Cooperative Sensors

In this dissertation, we present vision based scene interpretation methods for monitoring of people and vehicles, in real-time, within a busy environment using a forest of co-operative electro-optical (EO) sensors. We have developed novel video understanding algorithms with learning capability, to detect and categorize people and vehicles, track them with in a camera and hand-off this information across multiple networked cameras for multi-camera tracking. The ability to learn prevents the need for extensive manual intervention, site models and camera calibration, and provides adaptability to changing environmental conditions. For object detection and categorization in the video stream, a two step detection procedure is used. First, regions of interest are determined using a novel hierarchical background subtraction algorithm that uses color and gradient information for interest region detection. Second, objects are located and classified from within these regions using a weakly supervised learning mechanism based on co-training that employs motion and appearance features. The main contribution of this approach is that it is an online procedure in which separate views (features) of the data are used for co-training, while the combined view (all features) is used to make classification decisions in a single boosted framework. The advantage of this approach is that it requires only a few initial training samples and can automatically adjust its parameters online to improve the detection and classification performance. Once objects are detected and classified they are tracked in individual cameras. Single camera tracking is performed using a voting based approach that utilizes color and shape cues to establish correspondence in individual cameras. The tracker has the capability to handle multiple occluded objects. Next, the objects are tracked across a forest of cameras with non-overlapping views. This is a hard problem because of two reasons. First, the observations of an object are often widely separated in time and space when viewed from non-overlapping cameras. Secondly, the appearance of an object in one camera view might be very different from its appearance in another camera view due to the differences in illumination, pose and camera properties. To deal with the first problem, the system learns the inter-camera relationships to constrain track correspondences. These relationships are learned in the form of multivariate probability density of space-time variables (object entry and exit locations, velocities, and inter-camera transition times) using Parzen windows. To handle the appearance change of an object as it moves from one camera to another, we show that all color transfer functions from a given camera to another camera lie in a low dimensional subspace. The tracking algorithm learns this subspace by using probabilistic principal component analysis and uses it for appearance matching. The proposed system learns the camera topology and subspace of inter-camera color transfer functions during a training phase. Once the training is complete, correspondences are assigned using the maximum a posteriori (MAP) estimation framework using both the location and appearance cues. Extensive experiments and deployment of this system in realistic scenarios has demonstrated the robustness of the proposed methods. The proposed system was able to detect and classify targets, and seamlessly tracked them across multiple cameras. It also generated a summary in terms of key frames and textual description of trajectories to a monitoring officer for final analysis and response decision. This level of interpretation was the goal of our research effort, and we believe that it is a significant step forward in the development of intelligent systems that can deal with the complexities of real world scenarios

The Ethics of Police Body-Worn Cameras

Over the past decade, police departments in many countries have experimented with and increasingly adopted the use of police body-worn cameras. This article aims to examine the moral issues raised by the use of PBWCs, and to provide an overall assessment of the conditions under which the use of PBWCs is morally permissible. It first reviews the current evidence for the effects of using PBWCs. On the basis of this review the article sets out a teleological argument for the use of PBWCs. The final two sections of the article review two deontological objections to the use of PBWCs: the idea that use of PBWCs is based on or expresses disrespectful mistrust, and the idea that the use of PBWCs violates a right to privacy. The article argues that neither of these objections is persuasive, and concludes that we should conditionally accept and support the use of PBWCs

Comprehensive Survey and Analysis of Techniques, Advancements, and Challenges in Video-Based Traffic Surveillance Systems

The challenges inherent in video surveillance are compounded by a several factors, like dynamic lighting conditions, the coordination of object matching, diverse environmental scenarios, the tracking of heterogeneous objects, and coping with fluctuations in object poses, occlusions, and motion blur. This research endeavor aims to undertake a rigorous and in-depth analysis of deep learning- oriented models utilized for object identification and tracking. Emphasizing the development of effective model design methodologies, this study intends to furnish a exhaustive and in-depth analysis of object tracking and identification models within the specific domain of video surveillance