Better features to track by estimating the tracking convergence region

Reliably tracking key points and textured patches from frame to frame is the basic requirement for many bottomup computer vision algorithms. The problem of selecting the features that can be tracked well is addressed here. The Lucas-Kanade tracking procedure is commonly used. We propose a method to estimate the size of the tracking procedure convergence region for each feature. The features that have a wider convergence region around them should be tracked better by the tracker. The size of the convergence region as a new feature goodness measure is compared with the widely accepted Shi-Tomasi feature selection criteria

Smart City Traffic Management

In smart cities, traffic congestion is a significant challenge, leading to delays, hindrance to emergency vehicles, and localized pollution. Contributing factors include a surge in vehicles, inadequate infrastructure, system failures, and limited awareness of traffic signals. Diverse con- gestion identification techniques, such as image processing, laser tracking, and inductive loop systems, exist. However, this model centers on Infrared technology. It employs Infrared to gauge vehicle density, subsequently regulating traffic signals through ESP8266 NodeMCU, with data relayed to a central cloud system. The solution seamlessly integrates with existing models, offering rapid installation. Benefits encompass time savings for motorists, reduced traffic vio- lations, and effective congestion management, furthering emergency vehicle access and abating environmental impact. Challenges involve precision in Infrared-based density assessment, scala- bility testing, sustained maintenance, and collaboration with pertinent authorities. Real-world data and user feedback offer prospects for algorithmic refinement, while historical traffic analysis informs urban planning. Exploring Internet of Things (IoT) integration enhances its potential in reshaping urban traffic contro

3D Vehicle Extraction and Tracking from Multiple Viewpoints for Traffic Monitoring by using Probability Fusion Map

This paper presents a novel solution of vehicle occlusion and 3D measurement for traffic monitoring by data fusion from multiple stationary cameras. Comparing with single camera based conventional methods in traffic monitoring, our approach fuses video data from different viewpoints into a common probability fusion map (PFM) and extracts targets. The proposed PFM concept is efficient to handle and fuse data in order to estimate the probability of vehicle appearance, which is verified to be more reliable than single camera solution by real outdoor experiments. An AMF based shadowing modeling algorithm is also proposed in this paper in order to remove shadows on the road area and extract the proper vehicle regions

Feature-supported Multi-hypothesis Framework for Multi-object Tracking using Kalman Filter

A Kalman filter is a recursive estimator and has widely been used for tracking objects. However, unsatisfying tracking of moving objects is observed under complex situations (i.e. inter-object merge and split) which are challenging for classical Kalman filter. This paper describes a multi-hypothesis framework based on multiple features for tracking the moving objects under complex situations using Kalman Tracker. In this framework, a hypothesis (i.e. merge, split, new) is generated on the basis of contextual association probability which identifies the status of the moving objects in the respective occurrences. The association among the moving objects is computed by multi-featured similarity criteria which include spatial size, color and trajectory. Color similarity probability is computed by the correlation-weighted histogram intersection (CWHI). The similarity probabilities of the size and the trajectory are computed and combined with the fused color correlation. The accumulated association probability results in online hypothesis generation. This hypothesis assists Kalman tracker when complex situations appear in real-time tracking (i.e. traffic surveillance, pedestrian tracking). Our algorithm achieves robust tracking with 97.3% accuracy, and 0.07% covariance error in different real-time scenarios

Syntactic Method for Vehicles Movement Description and Analysis

The syntactic primitives and the description language can be used for assignment and analysis of vehicles movement. The paper introduces a method that allows spotting vehicles’ manoeuvres on and between traffic lanes, observing images, registered by a video camera. The analysis algorithms of the vehicles’ movement trajectories were considered in this paper as well

Image Processing in Road Traffic Analysis

The article presents an application of computer vision methods to traffic flow monitoring and road traffic analysis. The application is utilizing image-processing and pattern recognition methods designed and modified to the needs and constrains of road traffic analysis. These methods combined together gives functional capabilities of the system to monitor the road, to initiate automated vehicle tracking, to measure the speed, and to recognize number plates of a car. Software developed was applied in and approved with video monitoring system, based on standard CCTV cameras connected to wide area network computers

Enhancing airplane boarding procedure using vision based passenger classification

This paper presents the implementation of a new boarding strategy that exploits passenger and hand-luggage detection and classification to reduce the boarding time onto an airplane. A vision system has the main purpose of providing passengers data, in terms of agility coefficient and hand-luggage size to a seat assignment algorithm. The software is able to dynamically generate the passenger seat that reduces the overall boarding time while taking into account the current airplane boarding state. The motivation behind this work is to speed up of the passenger boarding using the proposed online procedure of seat assignment based on passenger and luggage classification. This method results in an enhancement of the boarding phase, in terms of both time and passenger experience. The main goal of this work is to demonstrate the usability of the proposed system in real conditions proving its performances in terms of reliability. Using a simple hardware and software setup, we performed several experiments recreating a gate entrance mock up and comparing the measurements with ground truth data to assess the reliability of the system

Monitoreo automático de carreteras mediante el uso de un sistema de detección, seguimiento y extracción de características básicas de vehículos con técnicas de visión por computador

En este proyecto se pretende implementar un sistema que usa técnicas de visión por computador para detectar, seguir y extraer características básicas de vehículos que se encuentran circulando en carreteras o autopistas. Para probar el sistema implementado se prevé usar como banco de prueba el uso de videos que han sido generados en vías rápidas o carreteras a través del uso de cámaras. Los resultados obtenidos con el sistema propuesto ofrecen información básica de los vehículos, tales como: dirección de su movimiento, carril en el que se desplaza, tamaño de la caja englobante que lo contiene, entre otros. La información que se genera de este proyecto podría servir como base para ayudar a monitorear el flujo vial en carreteras o autopistas, lo cual posteriormente facilitará las labores de control vial de los servidores públicos y la empresa privada. No es arriesgado pensar que en un futuro cercano se podría reutilizar o adaptar el sistema para realizar seguimiento vehicular en zonas urbanas. Adicionalmente, podría también considerarse su uso total o parcial para proyectos similares en túneles, vigilancia en video, sistemas de llamadas de emergencia, sistemas de peaje, entre otras aplicaciones afines