Feature-based image patch classification for moving shadow detection

Moving object detection is a first step towards many computer vision applications, such as human interaction and tracking, video surveillance, and traffic monitoring systems. Accurate estimation of the target object’s size and shape is often required before higher-level tasks (e.g., object tracking or recog nition) can be performed. However, these properties can be derived only when the foreground object is detected precisely. Background subtraction is a common technique to extract foreground objects from image sequences. The purpose of background subtraction is to detect changes in pixel values within a given frame. The main problem with background subtraction and other related object detection techniques is that cast shadows tend to be misclassified as either parts of the foreground objects (if objects and their cast shadows are bonded together) or independent foreground objects (if objects and shadows are separated). The reason for this phenomenon is the presence of similar characteristics between the target object and its cast shadow, i.e., shadows have similar motion, attitude, and intensity changes as the moving objects that cast them. Detecting shadows of moving objects is challenging because of problem atic situations related to shadows, for example, chromatic shadows, shadow color blending, foreground-background camouflage, nontextured surfaces and dark surfaces. Various methods for shadow detection have been proposed in the liter ature to address these problems. Many of these methods use general-purpose image feature descriptors to detect shadows. These feature descriptors may be effective in distinguishing shadow points from the foreground object in a specific problematic situation; however, such methods often fail to distinguish shadow points from the foreground object in other situations. In addition, many of these moving shadow detection methods require prior knowledge of the scene condi tions and/or impose strong assumptions, which make them excessively restrictive in practice. The aim of this research is to develop an efficient method capable of addressing possible environmental problems associated with shadow detection while simultaneously improving the overall accuracy and detection stability. In this research study, possible problematic situations for dynamic shad ows are addressed and discussed in detail. On the basis of the analysis, a ro bust method, including change detection and shadow detection, is proposed to address these environmental problems. A new set of two local feature descrip tors, namely, binary patterns of local color constancy (BPLCC) and light-based gradient orientation (LGO), is introduced to address the identified problematic situations by incorporating intensity, color, texture, and gradient information. The feature vectors are concatenated in a column-by-column manner to con struct one dictionary for the objects and another dictionary for the shadows. A new sparse representation framework is then applied to find the nearest neighbor of the test image segment by computing a weighted linear combination of the reference dictionary. Image segment classification is then performed based on the similarity between the test image and the sparse representations of the two classes. The performance of the proposed framework on common shadow detec tion datasets is evaluated, and the method shows improved performance com pared with state-of-the-art methods in terms of the shadow detection rate, dis crimination rate, accuracy, and stability. By achieving these significant improve ments, the proposed method demonstrates its ability to handle various problems associated with image processing and accomplishes the aim of this thesis

Verificación de vehículos mediante técnicas de visión artificial

En este trabajo, se proponen sistemas de verificación de vehículos mediante métodos basados en aprendizaje. En primer lugar se realiza un estudio del estado del arte para conocer los problemas actuales en la materia. Después, se muestra la arquitectura de los sistemas que se divide en dos etapas: extracción de características y clasificación. En la primera etapa se realiza una breve exposición de los tipos de características que se van a implementar (simetría, bordes, análisis de componentes principales (PCA) e histogramas de gradientes orientados (HOG)). La etapa de clasificación consiste en una explicación teórica de los clasificadores utilizados en nuestro sistema. Posteriormente, se realiza el desarrollo de estos sistemas, efectuando mejoras para cada uno de ellos. Para el sistema basado en simetría se plantean dos métodos diferentes, introduciéndose una mejora en el segundo método, que consiste en una diferenciación entre ejes compuestos por uno y dos píxeles, junto con una penalización en los valores de simetría para conseguir una mayor diferenciación entre las clases. Respecto al sistema basado en bordes, se utilizan únicamente bordes verticales, donde se analiza el uso de vectores reducidos. Por otra parte, se presenta el uso de la matriz de correlaciones para desarrollar el sistema basado en PCA. En el sistema basado en HOG se estudia qué parámetros son los adecuados para el descriptor en el caso particular de vehículos, proponiéndose descriptores eficientes basados en esta configuración, que pueden ser implementados en sistemas en tiempo real. Finalmente, con los resultados obtenidos en el paso previo se procede a un análisis para los distintos métodos presentando sus principales características y limitaciones.In this work, a vehicle verification systems using learning methods are proposed. First, a study of related work has been done. Afterwards, the arquitecture of these systems is explained. The arquitecure is divided in two stages: feature extraction and clasification. In the first stage, a brief summary of the different features that will be implemented (simmetry, edges, principal components analysis (PCA) and histograms of oriented gradients (HOG)) is given. The second stage is a theoretical explanation of the classifiers used in this system. Subsequently, the systems are developed with new improvements. Two different methods are proposed for the system based on symmetry. An improvement is introduced for the second method that is a differentiation between compounds axes by one and two pixels, also a penalty is introduced into the values of symmetry for greater differentiation between classes. Regarding the system based on edges, vertical edges are used, where the performance reducing the size of the vectors is analyzed. Moreover, the correlation matrix is used to develop the system based on PCA. In the system based on HOG, in the particular case of vehicles, appropiate parameters for the descriptor are studied, proposing efficient descriptors based on this configuration that can be implemented in real-time systems. Finally, the results obtained in the previous step are analyzed for each of the methods, and their main characteristics and limitations are described

Vehicle detection based on color analysis

Detecting moving objects in videos is an important task in many computer vision applications, including human interaction, traffic monitoring and Structural Health Monitoring. When having a stationary camera, a basic method to detect the objects of interest is background subtraction. However, precise moving object detection using such a method is an extremely difficult task in a varying environment. This paper introduces a new technique for detecting vehicles in a traffic scene based on frame differencing and color analysis for foreground region (moving region). The foreground region consists of pixels belonging to moving objects, shadows and possibly part of the background in the current frame. The image will be scanned in both horizontal and vertical directions to create threshold lines to eliminate pixels belonging to shadows and the background. The technique is fast and reliable for real-time applications and experimental results show the accuracy of the method