A Modified Frame Difference Method Using Correlation Coefficient for Background Subtraction

AbstractBackground subtraction is one of the most important step in video surveillance which is used in a number of real life applications such as surveillance, human machine interaction, optical motion capture and intelligent visual observation of animals, insects. Background subtraction is one of the preliminary stages which are used to differentiate the foreground objects from the relatively stationary background. Normally a pixel is considered as foreground if its value is greater than its value in the reference image. Hence, every pixel has to be compared to find the foreground and background pixel. This paper presents a technique which improves the frame difference method by first classifying the blocks in the frame as background and others using correlation coefficient. Further refinement is performed by performing pixel-level classification on blocks which are not considered as background. Experiments are conducted on standard data-sets and the performance measures shows good results in some critical conditions

Detecting and Shadows in the HSV Color Space using Dynamic Thresholds

The detection of moving objects in a video sequence is an essential step in almost all the systems of vision by computer. However, because of the dynamic change in natural scenes, the detection of movement becomes a more difficult task. In this work, we propose a new method for the detection moving objects that is robust to shadows, noise and illumination changes. For this purpose, the detection phase of the proposed method is an adaptation of the MOG approach where the foreground is extracted by considering the HSV color space. To allow the method not to take shadows into consideration during the detection process, we developed a new shade removal technique based on a dynamic thresholding of detected pixels of the foreground. The calculation model of the threshold is established by two statistical analysis tools that take into account the degree of the shadow in the scene and the robustness to noise.  Experiments undertaken on a set of video sequences showed that the method put forward provides better results compared to existing methods that are limited to using static thresholds

Lane Detection in Video-Based Intelligent Transportation Monitoring via Fast Extracting and Clustering of Vehicle Motion Trajectories

Lane detection is a crucial process in video-based transportation monitoring system. This paper proposes a novel method to detect the lane center via rapid extraction and high accuracy clustering of vehicle motion trajectories. First, we use the activity map to realize automatically the extraction of road region, the calibration of dynamic camera, and the setting of three virtual detecting lines. Secondly, the three virtual detecting lines and a local background model with traffic flow feedback are used to extract and group vehicle feature points in unit of vehicle. Then, the feature point groups are described accurately by edge weighted dynamic graph and modified by a motion-similarity Kalman filter during the sparse feature point tracking. After obtaining the vehicle trajectories, a rough k-means incremental clustering with Hausdorff distance is designed to realize the rapid online extraction of lane center with high accuracy. The use of rough set reduces effectively the accuracy decrease, which results from the trajectories that run irregularly. Experimental results prove that the proposed method can detect lane center position efficiently, the affected time of subsequent tasks can be reduced obviously, and the safety of traffic surveillance systems can be enhanced significantly

Histograma de orientación de gradientes aplicado al seguimiento múltiple de personas basado en video

El seguimiento múltiple de personas en escenas reales es un tema muy importante en el campo de Visión Computacional dada sus múltiples aplicaciones en áreas como en los sistemas de vigilancia, robótica, seguridad peatonal, marketing, etc., además de los retos inherentes que representa la identificación de personas en escenas reales como son la complejidad de la escena misma, la concurrencia de personas y la presencia de oclusiones dentro del video debido a dicha concurrencia. Existen diversas técnicas que abordan el problema de la segmentación de imágenes y en particular la identificación de personas, desde diversas perspectivas; por su parte el presente trabajo tiene por finalidad desarrollar una propuesta basada en Histograma de Orientación de Gradientes (HOG) para el seguimiento múltiple de personas basado en video. El procedimiento propuesto se descompone en las siguientes etapas: Procesamiento de Video, este proceso consiste en la captura de los frames que componen la secuencia de video, para este propósito se usa la librería OpenCV de tal manera que se pueda capturar la secuencia desde cualquier fuente; la siguiente etapa es la Clasificación de Candidatos, esta etapa se agrupa el proceso de descripción de nuestro objeto, que para el caso de este trabajo son personas y la selección de los candidatos, para esto se hace uso de la implementación del algoritmo de HOG; por último la etapa final es el Seguimiento y Asociación, mediante el uso del algoritmo de Kalman Filter, permite determinar las asociaciones de las secuencias de objetos previamente detectados. La propuesta se aplicó sobre tres conjuntos de datos, tales son: TownCentre (960x540px), TownCentre (1920x1080px) y PETS 2009, obteniéndose los resultados para precisión: 94.47%, 90.63% y 97.30% respectivamente. Los resultados obtenidos durante las experimentaciones validan la propuesta del modelo haciendo de esta una herramienta que puede encontrar múltiples campos de aplicación, además de ser una propuesta innovadora a nivel nacional dentro del campo de Vision Computacional.Tesi