Understanding Vehicular Traffic Behavior from Video: A Survey of Unsupervised Approaches

Recent emerging trends for automatic behavior analysis and understanding from infrastructure video are reviewed. Research has shifted from high-resolution estimation of vehicle state and instead, pushed machine learning approaches to extract meaningful patterns in aggregates in an unsupervised fashion. These patterns represent priors on observable motion, which can be utilized to describe a scene, answer behavior questions such as where is a vehicle going, how many vehicles are performing the same action, and to detect an abnormal event. The review focuses on two main methods for scene description, trajectory clustering and topic modeling. Example applications that utilize the behavioral modeling techniques are also presented. In addition, the most popular public datasets for behavioral analysis are presented. Discussion and comment on future directions in the field are also provide

Multimedia

The nowadays ubiquitous and effortless digital data capture and processing capabilities offered by the majority of devices, lead to an unprecedented penetration of multimedia content in our everyday life. To make the most of this phenomenon, the rapidly increasing volume and usage of digitised content requires constant re-evaluation and adaptation of multimedia methodologies, in order to meet the relentless change of requirements from both the user and system perspectives. Advances in Multimedia provides readers with an overview of the ever-growing field of multimedia by bringing together various research studies and surveys from different subfields that point out such important aspects. Some of the main topics that this book deals with include: multimedia management in peer-to-peer structures & wireless networks, security characteristics in multimedia, semantic gap bridging for multimedia content and novel multimedia applications

Automatic Behavior Analysis and Understanding of Collision Processes Using Video Sensors

RÉSUMÉ La sécurité routière est un des problèmes de société les plus importants à cause des multiples impacts et coûts des accidents de la route. Traditionnellement, le diagnostic de sécurité repose principalement sur les données historiques de collision. Cette approche réactive mène à remédier au problème de sécurité après que ses impacts sur la société soit déjà réalisés. Les analystes de la sécurité et les décideurs doivent attendre jusqu'à ce qu'un nombre suffisant de collisions (ce qui demande d’attendre habituellement au moins trois ans) soit collecté pour analyser ou mettre en place des mesures d’amélioration de la sécurité routière. Les méthodes substituts (« surrogate ») d'analyse de la sécurité constituent une approche alternative proactive qui s'appuie sur l'observation d’événements « dangereux » sans collision, souvent appelé accidents « évités de justesse » (« near misses ») ou « conflits ». Parmi ces approches, les techniques de conflits de trafic (TCT) reposent sur la collecte des données de conflit par des observateurs sur le terrain qui interprètent leur sévérité. Par conséquent, les TCT souffrent des variations de jugement des observateurs, de la difficulté de mesurer les indicateurs de sécurité en temps réel par les observateurs, et du coût de la collecte des données.----------ABSTRACTTraffic safety is one of the most important social issues due to the multiple costs of collisions. Traditionally, safety diagnosis depends mainly on historical collision data. This reactive approach leads to remedy the existing safety problem after the materialization of the induced social cost. Safety analysts and decision makers must wait till a sufficient number of collisions (typically at least 3 years of collision data) is collected to analyze and to devise countermeasures. Surrogate safety analysis is an alternative and proactive approach that relies on the observation of traffic events without a collision, in particular “unsafe” events often called “near misses” or “conflicts”. Among these approaches, traffic conflict techniques (TCT) rely mainly on field observers to identify conflicts and interpret their severity. Consequently, TCTs suffer from the variations of observer judgement, the cost of collecting conflict data, and the difficulty of measuring safety indicators in real time by the observers