Design of a Passenger Security and Safety System for the Kayoola EVs Bus

This research article was published by IEEE, 2021Kiira Motors Corporation seeks to avail customer satisfaction, by providing noteworthy passenger experience on its market entry product, the Kayoola EVs bus through deploying a passenger security and safety system to curtail rampant snags like passenger insecurity, loss of passenger property, shortcomings in management and accountability as well as the spread of contagious sicknesses like COVID-19 which are not alien occurrences on commuter taxis and buses in African cities. On this project, a comprehensive system was designed for remote CCTV video surveillance, video analysis for people detection, passenger count and social distance analysis, as well as digital contact tracing to solve the challenges. It denotes significant potential to improve the security of property and passengers, shrink the risk of the spread of contagious diseases, enable timely capture of contact tracing records and lessen the burden of management, monitoring and accountability for the numbers of passengers on buses for fleet owners

Video foreground extraction for mobile camera platforms

Foreground object detection is a fundamental task in computer vision with many applications in areas such as object tracking, event identification, and behavior analysis. Most conventional foreground object detection methods work only in a stable illumination environments using fixed cameras. In real-world applications, however, it is often the case that the algorithm needs to operate under the following challenging conditions: drastic lighting changes, object shape complexity, moving cameras, low frame capture rates, and low resolution images. This thesis presents four novel approaches for foreground object detection on real-world datasets using cameras deployed on moving vehicles.The first problem addresses passenger detection and tracking tasks for public transport buses investigating the problem of changing illumination conditions and low frame capture rates. Our approach integrates a stable SIFT (Scale Invariant Feature Transform) background seat modelling method with a human shape model into a weighted Bayesian framework to detect passengers. To deal with the problem of tracking multiple targets, we employ the Reversible Jump Monte Carlo Markov Chain tracking algorithm. Using the SVM classifier, the appearance transformation models capture changes in the appearance of the foreground objects across two consecutives frames under low frame rate conditions. In the second problem, we present a system for pedestrian detection involving scenes captured by a mobile bus surveillance system. It integrates scene localization, foreground-background separation, and pedestrian detection modules into a unified detection framework. The scene localization module performs a two stage clustering of the video data.In the first stage, SIFT Homography is applied to cluster frames in terms of their structural similarity, and the second stage further clusters these aligned frames according to consistency in illumination. This produces clusters of images that are differential in viewpoint and lighting. A kernel density estimation (KDE) technique for colour and gradient is then used to construct background models for each image cluster, which is further used to detect candidate foreground pixels. Finally, using a hierarchical template matching approach, pedestrians can be detected.In addition to the second problem, we present three direct pedestrian detection methods that extend the HOG (Histogram of Oriented Gradient) techniques (Dalal and Triggs, 2005) and provide a comparative evaluation of these approaches. The three approaches include: a) a new histogram feature, that is formed by the weighted sum of both the gradient magnitude and the filter responses from a set of elongated Gaussian filters (Leung and Malik, 2001) corresponding to the quantised orientation, which we refer to as the Histogram of Oriented Gradient Banks (HOGB) approach; b) the codebook based HOG feature with branch-and-bound (efficient subwindow search) algorithm (Lampert et al., 2008) and; c) the codebook based HOGB approach.In the third problem, a unified framework that combines 3D and 2D background modelling is proposed to detect scene changes using a camera mounted on a moving vehicle. The 3D scene is first reconstructed from a set of videos taken at different times. The 3D background modelling identifies inconsistent scene structures as foreground objects. For the 2D approach, foreground objects are detected using the spatio-temporal MRF algorithm. Finally, the 3D and 2D results are combined using morphological operations.The significance of these research is that it provides basic frameworks for automatic large-scale mobile surveillance applications and facilitates many higher-level applications such as object tracking and behaviour analysis

The Fourth Amendment in the Twenty-First Century: Technology, Privacy, and Human Emotions

Police and local political officials in Tampa FL argued that the FaceIt system promotes safety, but privacy advocates objected to the city\u27s recording or utilizing facial images without the victims\u27 consent, some staging protests against the FaceIt system. Privacy objects seem to be far more widely shared than this small protest might suggest

The Fourth Amendment in the Twenty-First Century: Technology, Privacy, and Human Emotions

Police and local political officials in Tampa FL argued that the FaceIt system promotes safety, but privacy advocates objected to the city\u27s recording or utilizing facial images without the victims\u27 consent, some staging protests against the FaceIt system. Privacy objects seem to be far more widely shared than this small protest might suggest

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

Towards sustainable transport: wireless detection of passenger trips on public transport buses

An important problem in creating efficient public transport is obtaining data about the set of trips that passengers make, usually referred to as an Origin/Destination (OD) matrix. Obtaining this data is problematic and expensive in general, especially in the case of buses because on-board ticketing systems do not record where and when passengers get off a bus. In this paper we describe a novel and inexpensive system that uses off-the-shelf Bluetooth hardware to accurately record passenger journeys. Here we show how our system can be used to derive passenger OD matrices, and additionally we show how our data can be used to further improve public transport services.Comment: 13 pages, 4 figures, 1 tabl