Text Localization in Video Using Multiscale Weber's Local Descriptor

In this paper, we propose a novel approach for detecting the text present in videos and scene images based on the Multiscale Weber's Local Descriptor (MWLD). Given an input video, the shots are identified and the key frames are extracted based on their spatio-temporal relationship. From each key frame, we detect the local region information using WLD with different radius and neighborhood relationship of pixel values and hence obtained intensity enhanced key frames at multiple scales. These multiscale WLD key frames are merged together and then the horizontal gradients are computed using morphological operations. The obtained results are then binarized and the false positives are eliminated based on geometrical properties. Finally, we employ connected component analysis and morphological dilation operation to determine the text regions that aids in text localization. The experimental results obtained on publicly available standard Hua, Horizontal-1 and Horizontal-2 video dataset illustrate that the proposed method can accurately detect and localize texts of various sizes, fonts and colors in videos.Comment: IEEE SPICES, 201

Recent Trends and Techniques in Text Detection and Text Localization in a Natural Scene: A Survey

Text information extraction from natural scene images is a rising area of research. Since text in natural scene images generally carries valuable details, detecting and recognizing scene text has been deemed essential for a variety of advanced computer vision applications. There has been a lot of effort put into extracting text regions from scene text images in an effective and reliable manner. As most text recognition applications have high demand of robust algorithms for detecting and localizing texts from a given scene text image, so the researchers mainly focus on the two important stages text detection and text localization. This paper provides a review of various techniques of text detection and text localization

Human object annotation for surveillance video forensics

A system that can automatically annotate surveillance video in a manner useful for locating a person with a given description of clothing is presented. Each human is annotated based on two appearance features: primary colors of clothes and the presence of text/logos on clothes. The annotation occurs after a robust foreground extraction stage employing a modified Gaussian mixture model-based approach. The proposed pipeline consists of a preprocessing stage where color appearance of an image is improved using a color constancy algorithm. In order to annotate color information for human clothes, we use the color histogram feature in HSV space and find local maxima to extract dominant colors for different parts of a segmented human object. To detect text/logos on clothes, we begin with the extraction of connected components of enhanced horizontal, vertical, and diagonal edges in the frames. These candidate regions are classified as text or nontext on the basis of their local energy-based shape histogram features. Further, to detect humans, a novel technique has been proposed that uses contourlet transform-based local binary pattern (CLBP) features. In the proposed method, we extract the uniform direction invariant LBP feature descriptor for contourlet transformed high-pass subimages from vertical and diagonal directional bands. In the final stage, extracted CLBP descriptors are classified by a trained support vector machine. Experimental results illustrate the superiority of our method on large-scale surveillance video data

ACCURATE TRACKING OF OBJECTS USING LEVEL SETS

Our current work presents an approach to tackle the challenging task of tracking objects in Internet videos taken from large web repositories such as YouTube. Such videos more often than not, are captured by users using their personal hand-held cameras and cellphones and hence suffer from problems such as poor quality, camera jitter and unconstrained lighting and environmental settings. Also, it has been observed that events being recorded by such videos usually contain objects moving in an unconstrained fashion. Hence, tracking objects in Internet videos is a very challenging task in the field of computer vision since there is no a-priori information about the types of objects we might encounter, their velocities while in motion or intrinsic camera parameters to estimate the location of object in each frame. Hence, in this setting it is clearly not possible to model objects as single homogenous distributions in feature space. The feature space itself cannot be fixed since different objects might be discriminable in different sub-spaces. Keeping these challenges in mind, in the current proposed technique, each object is divided into multiple fragments or regions and each fragment is represented in Gaussian Mixture model (GMM) in a joint feature-spatial space. Each fragment is automatically selected from the image data by adapting to image statistics using a segmentation technique. We introduce the concept of strength map which represents a probability distribution of the image statistics and is used to detecting the object. We extend our goal of tracking object to tracking them with accurate boundaries thereby making the current task more challenging. We solve this problem by modeling the object using a level sets framework, which helps in preserving accurate boundaries of the object and as well in modeling the target object and background. These extracted object boundaries are learned dynamically over time, enabling object tracking even during occlusion. Our proposed algorithm performs significantly better than any of the existing object modeling techniques. Experimental results have been shown in support of this claim. Apart from tracking, the present algorithm can also be applied to different scenarios. One such application is contour-based object detection. Also, the idea of strength map was successfully applied to track objects such as vessels and vehicles on a wide range of videos, as a part of the summer internship program