Super-resolução em vídeos de baixa qualidade para aplicações forenses, de vigilância e móveis

Orientadores: Siome Klein Goldenstein, Anderson de Rezende RochaTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Algoritmos de super-resolução (SR) são métodos para obter um aumento da resolução de imagens compostas por pixels. Na super-resolução por múltiplas imagens, um conjunto de imagens de baixa resolução de uma cena é combinado para construir uma imagem de resolução superior. Super-resolução é uma solução barata para superar as limitações dos sistemas de aquisição de imagens, e pode ser útil em diversos casos em que o dispositivo não pode ser melhorado ou substituído - mas em que é possível obter diversas capturas da mesma cena. Neste trabalho, é explorada a super-resolução por múltiplas imagens para imagens naturais, em cenários nos quais é possível obter diversas imagens de uma cena. São propostas cinco variações de um método que explora propriedades geométricas de múltiplas imagens de baixa resolução para combiná-las em uma imagem de resolução superior; duas variações de um método que combina técnicas de inpainting e super-resolução; e mais três variações de um método que utiliza filtros adaptativos e regularização para resolver um problema de mínimos quadrados. Super-resolução por múltiplas imagens é possível quando existe movimento e informações não redundantes entre as imagens de baixa resolução. Entretanto, combiná-las em uma imagem de resolução superior pode não ser computacionalmente viável por técnicas complexas de super-resolução. A primeira aplicação dos métodos propostos é para um conjunto de imagens capturadas pelos dispositivos móveis mais recentes. Este tipo de ambiente requer algoritmos eficazes que sejam executados rapidamente e utilizando baixo consumo de memória. A segunda aplicação é na Ciência Forense. Câmeras de vigilância espalhadas pelas cidades poderiam fornecer dicas importantes para identificar um suspeito, por exemplo, em uma cena de crime. Entretanto, o reconhecimento dos caracteres de placas veiculares é especialmente difícil quando a resolução das imagens é baixa. Por isso, este trabalho também propõe um arcabouço que realiza a super-resolução de placas veiculares em vídeos reais de vigilância, capturados por câmeras de baixa qualidade e não projetadas especificamente para esta tarefa, ajudando o especialista forense a compreender um evento de interesse. O arcabouço realiza todas as etapas necessárias para rastrear, alinhar, reconstruir e reconhecer automaticamente os caracteres de uma placa suspeita. O usuário recebe, como saída, a imagem de alta resolução reconstruída, mais rica em detalhes, e também a sequência de caracteres reconhecida automaticamente nesta imagem. São apresentadas validações quantitativas e qualitativas dos algoritmos propostos e de suas aplicações. Os experimentos mostram, por exemplo, que é possível aumentar o número de caracteres reconhecidos corretamente, colocando o arcabouço proposto como uma ferramenta importante para fornecer aos peritos uma solução para o reconhecimento de placas veiculares sob condições adversas de aquisição. Por fim, também é sugerido o número mínimo de imagens a ser utilizada como entrada em cada aplicaçãoAbstract: Super-resolution (SR) algorithms are methods for achieving high-resolution (HR) enlargements of pixel-based images. In multi-frame super resolution, a set of low-resolution (LR) images of a scene are combined to construct an image with higher resolution. Super resolution is an inexpensive solution to overcome the limitations of image acquisition hardware systems, and can be useful in several cases in which the device cannot be upgraded or replaced, but multiple frames of the same scene can be obtained. In this work, we explore SR possibilities for natural images, in scenarios wherein we have multiple frames of a same scene. We design and develop five variations of an algorithm which rely on exploring geometric properties in order to combine pixels from LR observations into an HR grid; two variations of a method that combines inpainting techniques to multi-frame super resolution; and three variations of an algorithm that uses adaptive filtering and Tikhonov regularization to solve a least-square problem. Multi-frame super resolution is possible when there is motion and non-redundant information from LR observations. However, combining a large number of frames into a higher resolution image may not be computationally feasible by complex super-resolution techniques. The first application of the proposed methods is in consumer-grade photography with a setup in which several low-resolution images gathered by recent mobile devices can be combined to create a much higher resolution image. Such always-on low-power environment requires effective high-performance algorithms, that run fastly and with a low-memory footprint. The second application is in Digital Forensic, with a setup in which low-quality surveillance cameras throughout the cities could provide important cues to identify a suspect vehicle, for example, in a crime scene. However, license-plate recognition is especially difficult under poor image resolutions. Hence, we design and develop a novel, free and open-source framework underpinned by SR and Automatic License-Plate Recognition (ALPR) techniques to identify license-plate characters in low-quality real-world traffic videos, captured by cameras not designed for the ALPR task, aiding forensic analysts in understanding an event of interest. The framework handles the necessary conditions to identify a target license plate, using a novel methodology to locate, track, align, super resolve, and recognize its alphanumerics. The user receives as outputs the rectified and super-resolved license-plate, richer in details, and also the sequence of license-plates characters that have been automatically recognized in the super-resolved image. We present quantitative and qualitative validations of the proposed algorithms and its applications. Our experiments show, for example, that SR can increase the number of correctly recognized characters posing the framework as an important step toward providing forensic experts and practitioners with a solution for the license-plate recognition problem under difficult acquisition conditions. Finally, we also suggest a minimum number of images to use as input in each applicationDoutoradoCiência da ComputaçãoDoutor em Ciência da Computação1197478,146886153996/3-2015CAPESCNP

TSTTC: A Large-Scale Dataset for Time-to-Contact Estimation in Driving Scenarios

Time-to-Contact (TTC) estimation is a critical task for assessing collision risk and is widely used in various driver assistance and autonomous driving systems. The past few decades have witnessed development of related theories and algorithms. The prevalent learning-based methods call for a large-scale TTC dataset in real-world scenarios. In this work, we present a large-scale object oriented TTC dataset in the driving scene for promoting the TTC estimation by a monocular camera. To collect valuable samples and make data with different TTC values relatively balanced, we go through thousands of hours of driving data and select over 200K sequences with a preset data distribution. To augment the quantity of small TTC cases, we also generate clips using the latest Neural rendering methods. Additionally, we provide several simple yet effective TTC estimation baselines and evaluate them extensively on the proposed dataset to demonstrate their effectiveness. The proposed dataset is publicly available at https://open-dataset.tusen.ai/TSTTC.Comment: 19 pages, 9 figure

Parking lot monitoring system using an autonomous quadrotor UAV

The main goal of this thesis is to develop a drone-based parking lot monitoring system using low-cost hardware and open-source software. Similar to wall-mounted surveillance cameras, a drone-based system can monitor parking lots without affecting the flow of traffic while also offering the mobility of patrol vehicles. The Parrot AR Drone 2.0 is the quadrotor drone used in this work due to its modularity and cost efficiency. Video and navigation data (including GPS) are communicated to a host computer using a Wi-Fi connection. The host computer analyzes navigation data using a custom flight control loop to determine control commands to be sent to the drone. A new license plate recognition pipeline is used to identify license plates of vehicles from video received from the drone

Vehicle Speed Estimation Using Gaussian Mixture Model and Kalman Filter

Many countries use traffic enforcement camera to monitor the speed limit and capture over speed violations. The main objective of such a system is to enforce the speed limits which results in the reduction of number of accidents, fatalities, and serious injuries. Traditionally, the task is carried out manually by the enforcement agencies with the help of specialized hardware such as radar and camera. To automate the process, an efficient and robust solution is needed. Vehicle detection, tracking and speed estimation are the main tasks in an automated system which are not trivial. In this paper, we address the problem of vehicle detection, tracking, and speed estimation using a single fixed camera. A background subtraction method based on the Gaussian Mixture Model (GMM) is employed to detect vehicles because of its capability in dealing with complex backgrounds and variations in the appearance due to illumination and scale. Next, the detected vehicles are tracked in each frame by using the Kalman Filter. Finally, an estimate the speed of each vehicle is determined by using the perspective geometry model. The complete system is tested at our university campus and the results are promising

A Low Cost and Computationally Efficient Approach for Occlusion Handling in Video Surveillance Systems

In the development of intelligent video surveillance systems for tracking a vehicle, occlusions are one of the major challenges. It becomes difficult to retain features during occlusion especially in case of complete occlusion. In this paper, a target vehicle tracking algorithm for Smart Video Surveillance (SVS) is proposed to track an unidentified target vehicle even in case of occlusions. This paper proposes a computationally efficient approach for handling occlusions named as Kalman Filter Assisted Occlusion Handling (KFAOH) technique. The algorithm works through two periods namely tracking period when no occlusion is seen and detection period when occlusion occurs, thus depicting its hybrid nature. Kanade-Lucas-Tomasi (KLT) feature tracker governs the operation of algorithm during the tracking period, whereas, a Cascaded Object Detector (COD) of weak classifiers, specially trained on a large database of cars governs the operation during detection period or occlusion with the assistance of Kalman Filter (KF). The algorithm’s tracking efficiency has been tested on six different tracking scenarios with increasing complexity in real-time. Performance evaluation under different noise variances and illumination levels shows that the tracking algorithm has good robustness against high noise and low illumination. All tests have been conducted on the MATLAB platform. The validity and practicality of the algorithm are also verified by success plots and precision plots for the test cases

FARSEC: A Reproducible Framework for Automatic Real-Time Vehicle Speed Estimation Using Traffic Cameras

Estimating the speed of vehicles using traffic cameras is a crucial task for traffic surveillance and management, enabling more optimal traffic flow, improved road safety, and lower environmental impact. Transportation-dependent systems, such as for navigation and logistics, have great potential to benefit from reliable speed estimation. While there is prior research in this area reporting competitive accuracy levels, their solutions lack reproducibility and robustness across different datasets. To address this, we provide a novel framework for automatic real-time vehicle speed calculation, which copes with more diverse data from publicly available traffic cameras to achieve greater robustness. Our model employs novel techniques to estimate the length of road segments via depth map prediction. Additionally, our framework is capable of handling realistic conditions such as camera movements and different video stream inputs automatically. We compare our model to three well-known models in the field using their benchmark datasets. While our model does not set a new state of the art regarding prediction performance, the results are competitive on realistic CCTV videos. At the same time, our end-to-end pipeline offers more consistent results, an easier implementation, and better compatibility. Its modular structure facilitates reproducibility and future improvements

Development of Smart Plate Number Recognition System for Fast Cars with Web Application

Traffic law violation has been recognized as a major cause for road accidents in most parts of the world with majority occurring in developing countries. Even with the presence of rules and regulations stipulated against this, violators are still on the increase. This is due to the fact that the rules are not properly enforced by appropriate authorities in those parts of the world. Therefore, a system needs to be designed to assist law enforcement agencies to impose these rules to improve road safety and reduce road accidents. This work uses a Vehicle Plate Number Recognition (VNPR) system which is a real-time embedded system to automatically recognize license plate numbers. It provides an alternative means to VPNR using an open-source library known as openCV. The main aim of the system is to use image processing to identify vehicles violating traffic by their plate numbers. It consists of an IR sensor for detecting the vehicle. During testing, a minimum time was set for the sensor to detect the object which was recorded by the microprocessor. Once it was less than the set time, the camera was triggered to capture the plate number and store the image on the Raspberry Pi. The image captured is processed by the Raspberry Pi to extract the numbers on the image. The numbers on the capture imaged were viewed on a web page via an IP address. The system if implemented can be used to improve road safety and control traffic of emerging smart cities. It will also be used to apply appropriate sanctions for traffic law violators