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

Multiple Instance Learning: A Survey of Problem Characteristics and Applications

Multiple instance learning (MIL) is a form of weakly supervised learning where training instances are arranged in sets, called bags, and a label is provided for the entire bag. This formulation is gaining interest because it naturally fits various problems and allows to leverage weakly labeled data. Consequently, it has been used in diverse application fields such as computer vision and document classification. However, learning from bags raises important challenges that are unique to MIL. This paper provides a comprehensive survey of the characteristics which define and differentiate the types of MIL problems. Until now, these problem characteristics have not been formally identified and described. As a result, the variations in performance of MIL algorithms from one data set to another are difficult to explain. In this paper, MIL problem characteristics are grouped into four broad categories: the composition of the bags, the types of data distribution, the ambiguity of instance labels, and the task to be performed. Methods specialized to address each category are reviewed. Then, the extent to which these characteristics manifest themselves in key MIL application areas are described. Finally, experiments are conducted to compare the performance of 16 state-of-the-art MIL methods on selected problem characteristics. This paper provides insight on how the problem characteristics affect MIL algorithms, recommendations for future benchmarking and promising avenues for research

Object Tracking

Object tracking consists in estimation of trajectory of moving objects in the sequence of images. Automation of the computer object tracking is a difficult task. Dynamics of multiple parameters changes representing features and motion of the objects, and temporary partial or full occlusion of the tracked objects have to be considered. This monograph presents the development of object tracking algorithms, methods and systems. Both, state of the art of object tracking methods and also the new trends in research are described in this book. Fourteen chapters are split into two sections. Section 1 presents new theoretical ideas whereas Section 2 presents real-life applications. Despite the variety of topics contained in this monograph it constitutes a consisted knowledge in the field of computer object tracking. The intention of editor was to follow up the very quick progress in the developing of methods as well as extension of the application