Deep Learning for Logo Detection: A Survey

When logos are increasingly created, logo detection has gradually become a research hotspot across many domains and tasks. Recent advances in this area are dominated by deep learning-based solutions, where many datasets, learning strategies, network architectures, etc. have been employed. This paper reviews the advance in applying deep learning techniques to logo detection. Firstly, we discuss a comprehensive account of public datasets designed to facilitate performance evaluation of logo detection algorithms, which tend to be more diverse, more challenging, and more reflective of real life. Next, we perform an in-depth analysis of the existing logo detection strategies and the strengths and weaknesses of each learning strategy. Subsequently, we summarize the applications of logo detection in various fields, from intelligent transportation and brand monitoring to copyright and trademark compliance. Finally, we analyze the potential challenges and present the future directions for the development of logo detection to complete this survey

Logo recognition in videos: an automated brand analysis system

Every year companies spend a sizeable budget on marketing, a large portion of which is spent on advertisement of their product brands on TV broadcasts. These physical advertising artifacts are usually emblazoned with the companies' name, logo, and their trademark brand. Given these astronomical numbers, companies are extremely keen to verify that their brand has the level of visibility they expect for such expenditure. In other words advertisers, in particular, like to verify that their contracts with broadcasters are fulfilled as promised since the price of a commercial depends primarily on the popularity of the show it interrupts or sponsors. Such verifications are essential to major companies in order to justify advertising budgets and ensure their brands achieve the desired level of visibility. Currently, the verification of brand visibility occurs manually by human annotators who view a broadcast and annotate every appearance of a companies' trademark in the broadcast. In this thesis a novel brand logo analysis system which uses shape-based matching and scale invariant feature transform (SIFT) based matching on graphics processing unit (GPU) is proposed developed and tested. The system is described for detection and retrieval of trademark logos appearing in commercial videos. A compact representation of trademark logos and video frame content based on global (shape-based) and local (scale invariant feature transform (SIFT)) feature points is proposed. These representations can be used to robustly detect, recognize, localize, and retrieve trademarks as they appear in a variety of different commercial video types. Classification of trademarks is performed by using shaped-based matching and matching a set of SIFT feature descriptors for each trademark instance against the set of SIFT features detected in each frame of the video. Our system can automatically recognize the logos in video frames in order to summarize the logo content of the broadcast with the detected size, position and score. The output of the system can be used to summarize or check the time and duration of commercial video blocks on broadcast or on a DVD. Experimental results are provided, along with an analysis of the processed frames. Results show that our proposed technique is efficient and effectively recognizes and classifies trademark logos

Automatic visual detection of human behavior: a review from 2000 to 2014

Due to advances in information technology (e.g., digital video cameras, ubiquitous sensors), the automatic detection of human behaviors from video is a very recent research topic. In this paper, we perform a systematic and recent literature review on this topic, from 2000 to 2014, covering a selection of 193 papers that were searched from six major scientific publishers. The selected papers were classified into three main subjects: detection techniques, datasets and applications. The detection techniques were divided into four categories (initialization, tracking, pose estimation and recognition). The list of datasets includes eight examples (e.g., Hollywood action). Finally, several application areas were identified, including human detection, abnormal activity detection, action recognition, player modeling and pedestrian detection. Our analysis provides a road map to guide future research for designing automatic visual human behavior detection systems.This work is funded by the Portuguese Foundation for Science and Technology (FCT - Fundacao para a Ciencia e a Tecnologia) under research Grant SFRH/BD/84939/2012

A Study On Information Retrieval Systems

A video is a key component of today's multimedia applications,  including Video Cassette Recording (VCR), Video-on-Demand (VoD), and virtual walkthrough. This happens supplementary with the fast amplification in video skill (Rynson W.H. Lau et al. 2000). Owing to innovation's progress in the  media, computerized TV, and data frameworks, an immense measure of video information is now exhaustively realistic (Walid G. Aref et al. 2003). The startling advancement in computerized video content has made entrée and moves the data in a tremendous video database a muddled and sensible issue (Chih-Wen Su et al. 2005). Therefore, the necessity for creating devices and frameworks that can effectively investigate the most needed video content, has evoked a great deal of interest among analysts. Sports video has been chosen as the prime application in this proposition since it has attracted viewers around the world

Diseño de herramientas de apoyo para la detección de logotipos en secuencias de video

Este Trabajo Fin de Grado se ha realizado usando herramientas y conceptos de visión por ordenador para poder desarrollar métodos analíticos que permitan procesar una secuencia de video y obtener distintos tipos de parámetros o datos que de forma independiente o encadenados puedan llevar a realizar detecciones de logos (precargados o no) en los distintos fotogramas de la secuencia a procesar. El trabajo no se realiza sólo sobre un concepto dentro de la visión por ordenador y el procesado de imagen, sino que se intentan abarcar el máximo de herramientas y conceptos que pueden ser utilizados para detectar un logo, ya sean de color o forma. El método comienza definiendo tres pasos de pre-procesado que, motivados por las heurísticas del diseño, determinan las áreas donde un logo es más susceptible de ser localizado. Específicamente, los métodos usados son estrategias basadas en técnicas estructurales, saliencia y color que vayan reduciendo las zonas donde se ejecutarán las tareas de detección. Además, una detección de regiones estáticas en la secuencia evita detecciones en éstas áreas. En este proyecto, la detección de logotipos se logra mediante una serie de pasos, siendo el primero y más innovador el preprocesado, seguido del uso de segmentado de la imagen y matching de puntos de interés para alcanzar el reconocimiento correcto de un logotipo, que luego será revisado por varias técnicas incluyendo un módulo de perspectiva que detecta si el match está en la perspectiva general de la toma. Los logos se detectan midiendo el grado de similitud entre la plantilla transformada y el área candidata. Los resultados experimentales en una serie de secuencias elegidas validan parcialmente el diseño y método para transmisiones futbolísticas. Aunque por otro lado, los resultados muestran las limitaciones y problemas del método al analizar secuencias de otros deportes. Además, también se incluyen experimentos preliminares del uso de éste método en la generación de estadísticas enfocadas al análisis publicitario, dando resultados prometedores. En términos generales, los resultados sugieren que el uso de técnicas de pre-procesado puede ayudar en la labor de detección automática de logotipos.This work describes an automatic method for the detection of brand logos in sport sequences. The work starts by studying the solutions existing in the state-of-the art in the topic. From this study a set of conclusions is derived, and these are used to define the design of the proposed method. The method starts by defining three pre-processing methods which—motivated by design-heuristics—determine the spatial areas on which a logo is prone to be placed. Specifically, the methods use colour, structural and saliency based strategies to constrain the areas on which the logo detection process takes place. On the candidate areas—those prone to contain a logo—, a classical point-of-interest matching strategy is used to relate the candidate instances with a preload logo template. From these matches, an affine correction of the template is derived. Logos are detected by measuring the similarity between the transformed template and the candidate areas. Experimental results on a set of candidate sequences partially validate the design and development of the method for soccer sequences. However, results also illustrate the method’s drawbacks and limitations when analysing sequences of alternative sports. Furthermore, preliminary experiments on the use of the method for the generation of publicity statistics are also included, obtaining promising results. In overall, results suggest that the use of pre-processing techniques may help in the task of automatic logo detection