Over speed detection using Artificial Intelligence

Over speeding is one of the most common traffic violations. Around 41 million people are issued speeding tickets each year in USA i.e one every second. Existing approaches to detect over- speeding are not scalable and require manual efforts. In this project, by the use of computer vision and artificial intelligence, I have tried to detect over speeding and report the violation to the law enforcement officer. It was observed that when predictions are done using YoloV3, we get the best results

Sense and Avoid Characterization of the Independent Configurable Architecture for Reliable Operations of Unmanned Systems

AbstractIndependent Configurable Architecture for Reliable Operations of Unmanned Systems (ICAROUS) is a distributed software architecture developed by NASA Langley Research Center to enable safe autonomous UAS operations. ICAROUS consists of a collection formally verified core algorithms for path planning, traffic avoidance, geofence handling, and decision making that interface with an autopilot system through a publisher-subscriber middleware. The ICAROUS Sense and Avoid Characterization (ISAAC) test was designed to evaluate the performance of the onboard Sense and Avoid (SAA) capability to detect potential conflicts with other aircraft and autonomously maneuver to avoid collisions, while remaining within the airspace boundaries of the mission. The ISAAC tests evaluated the impact of separation distances and alerting times on SAA performance. A preliminary analysis of the effects of each parameter on key measures of performance is conducted, informing the choice of appropriate parameter values for different small Unmanned Aircraft Systems (sUAS) applications. Furthermore, low-power Automatic Dependent Surveillance Broadcast (ADS-B) is evaluated for potential use to enable autonomous sUAS to sUAS deconflictions as well as to provide usable warnings for manned aircraft without saturating the frequency spectrum

A Novel Approach in Analyzing Traffic Flow by Extreme Learning Machine Method

The objective of this study is to detect abnormal behaviours of moving objects captured in highway traffic flow footages, classify them by using artificial learning methods, and lastly to predict the future thereof (regression). To this end, the system being the object of the design and application consists of three stages. In the first stage, to detect the moving object in the video, background/foreground segmentation method of Mixture of Gaussian (MOG), and to track the moving object, Kalman Filter-Hungarian algorithm method have been used. In the second stage, by using the coordinates of the object, such details as location, distance in terms of time, and speed of the object are obtained, and by using total pixel count data relating to the shape of the object are obtained. The software based on the specifically elaborated algorithm compares these data with the data in the table of rules set down for the road under surveillance, and generates an attribute table comprising anomalies of the objects in the video. In the last stage, however, the data included in the attribute table have been classified and predictions by the artificial learning method, Extreme Learning Machine (ELM) made

Enforcement Guide: Near Shore Artisanal Fisheries

We need healthy oceans to support our way of life. Unfortunately, fish stocks are under growing pressure and the need to find innovative and pragmatic resource management strategies is more important than ever. Disregard for fisheries and environmental laws is common and if we are to succeed in reversing the declining trend, we must draft relevant regulations, design and fund comprehensive enforcement programs and cultivate a culture of compliance. Historically, marine law enforcement has been the competency of Naval and Coast Guard authorities; however, many fishery and park agencies, who lack training, equipment, and at times controlling legal authority, are tasked with fisheries management and enforcement. Complicating matters, most agencies are understaffed; lack budgetary resources, and possess limited authority (i.e. power of arrest and the ability to use force). WildAid in cooperation with The Nature Conservancy developed this guide to assist managers in designing a cost effective enforcement strategy for near shore artisanal fisheries. This document is not a recompilation of literature, but a practical guide based on our experience in the Eastern and Western Pacific. Generally, an enforcement system is designed to monitor all activities within a given area ranging from tourism, investigation, and transportation to fisheries; however, this guide will focus primarily on near shore artisanal fisheries. The objectives of this guide are three-fold:1. Examine all factors considered for the design and operation of a marine law enforcement system; 2. Illustrate key components of an enforcement system and evaluate surveillance technology and patrol equipment options; 3. Guide managers in the design and implementation of an enforcement system.In summary, it aims to equip managers with the tools needed to strengthen fisheries management and design enforcement systems that are practical, affordable and feasible to implement in a timely manner. Fisheries enforcement requires a holistic approach that accounts for surveillance, interdiction, systematic training, education and outreach and lastly, meaningful sanctions. Although it explores many surveillance technologies and management tools, this guide more importantly provides a blueprint for the capacity building and professionalization of enforcement officers, who truly are the core component of any fisheries enforcement program

An investigation into hazard-centric analysis of complex autonomous systems

This thesis proposes a hypothesis that a conventional, and essentially manual, HAZOP process can be improved with information obtained with model-based dynamic simulation, using a Monte Carlo approach, to update a Bayesian Belief model representing the expected relations between cause and effects – and thereby produce an enhanced HAZOP. The work considers how the expertise of a hazard and operability study team might be augmented with access to behavioural models, simulations and belief inference models. This incorporates models of dynamically complex system behaviour, considering where these might contribute to the expertise of a hazard and operability study team, and how these might bolster trust in the portrayal of system behaviour. With a questionnaire containing behavioural outputs from a representative systems model, responses were collected from a group with relevant domain expertise. From this it is argued that the quality of analysis is dependent upon the experience and expertise of the participants but this might be artificially augmented using probabilistic data derived from a system dynamics model. Consequently, Monte Carlo simulations of an improved exemplar system dynamics model are used to condition a behavioural inference model and also to generate measures of emergence associated with the deviation parameter used in the study. A Bayesian approach towards probability is adopted where particular events and combinations of circumstances are effectively unique or hypothetical, and perhaps irreproducible in practice. Therefore, it is shown that a Bayesian model, representing beliefs expressed in a hazard and operability study, conditioned by the likely occurrence of flaw events causing specific deviant behaviour from evidence observed in the system dynamical behaviour, may combine intuitive estimates based upon experience and expertise, with quantitative statistical information representing plausible evidence of safety constraint violation. A further behavioural measure identifies potential emergent behaviour by way of a Lyapunov Exponent. Together these improvements enhance the awareness of potential hazard cases

Geometric models for video surveillance in road environments: vehicle tailgating detection

Traffic accidents constitute one of the main causes of death in many countries. Despite the current efforts devoted to mitigate the effects of road incidents, there are still some variables affecting this problem which are not yet under control or regulation. Spain, for instance, still lacks official regulations about especially risky driving behaviours, such as tailgating. In many cases, the rationale behind is that these behaviours are hard or expensive to detect reliably, thus limiting the extent of the automatic detection systems. This paper proposes a method to identify certain elements in road scenarios, define geometric models that allow computing quantitative measures of the scene and, consequently, detect offending driving behaviours. In this work, we have focused on the particular case of study of tailgating detection. However, the proposed geometric models might become the basis of many other useful applications.Ingeniería de Sistemas Audiovisuale

Next-gen traffic surveillance: AI-assisted mobile traffic violation detection system

Road traffic accidents pose a significant global public health concern, leading to injuries, fatalities, and vehicle damage. Approximately 1,3 million people lose their lives daily due to traffic accidents [World Health Organization, 2022]. Addressing this issue requires accurate traffic law violation detection systems to ensure adherence to regulations. The integration of Artificial Intelligence algorithms, leveraging machine learning and computer vision, has facilitated the development of precise traffic rule enforcement. This paper illustrates how computer vision and machine learning enable the creation of robust algorithms for detecting various traffic violations. Our model, capable of identifying six common traffic infractions, detects red light violations, illegal use of breakdown lanes, violations of vehicle following distance, breaches of marked crosswalk laws, illegal parking, and parking on marked crosswalks. Utilizing online traffic footage and a self-mounted on-dash camera, we apply the YOLOv5 algorithm's detection module to identify traffic agents such as cars, pedestrians, and traffic signs, and the strongSORT algorithm for continuous interframe tracking. Six discrete algorithms analyze agents' behavior and trajectory to detect violations. Subsequently, an Identification Module extracts vehicle ID information, such as the license plate, to generate violation notices sent to relevant authorities