Sharif Digital Repository

Tracking by detection is the topic of recent research that has received considerable attention in computer vision community. Mainly off-line classification methods have been used, however, they perform weakly in the case of appearance changes. Training theclassifier incrementally and in an online manner solves this problem, but nevertheless, raises drifting due to soft or hard labeling in the online adaptation. In this paper a novel semi-supervised online tracking algorithm based on manifold assumption is proposed. Target object and background patches lie near low-dimensional manifolds. This motivates us to make use of the intrinsic structure of data in classification, and benefit from the... 

In this paper, a novel approach for foreground extraction has been proposed based on a popular three-dimensional imaging technique in optics, called integral imaging. In this approach, multiple viewpoint images captured from a three-dimensional scene are used to extract range information of the scene and effectively extract an object or a person, even in the presence of heavy occlusion. The algorithm consists of two parts: depth estimation and reconstruction of the targeted object at the estimated depth distance. Further processing of the resulting reconstructed image can lead to the detection of a face or a pedestrian in the scene, which may not otherwise be detectable due to partial... 

In recent years, processing the images that contain human faces has been a growing research interest because of establishment and development of automatic methods especially in security applications, compression, and perceptual user interface. In this paper, a new method has been proposed for multiple face detection and tracking in video frames. The proposed method uses skin color, edge and shape information, face detection, and dynamic movement analysis of faces for more accurate real-time multiple face detection and tracking purposes. One of the main advantages of the proposed method is its robustness against usual challenges in face tracking such as scaling, rotation, scene changes, fast... 

Object tracking is a difficult and primary task in many video processing applications. Because of the diversity of various video processing tasks, there exists no optimum method that can perform properly for all applications. Histogram-based particle filtering is one of the most successfu1 object tracking methods. However, for dealing with visual tracking in real world conditions (such as changes in illumination and pose) is still a challenging task. In this paper, we have proposed a color-based adaptive histogram particle filtering method that can update the target model. We have used the Bhattacharyya coefficients to measure the likelihood between two color histograms. Our experimental... 

In this paper, a novel moving object modeling suitable for multicamera correspondence is introduced. Taking into consideration the color and motion features of foreground objects in each independent video stream, our method segments the existing moving objects and constructs a graph-based structure to maintain the relational information of each segment. Using such graph structures reduces our correspondence problem to a subgraph optimal isomorphism problem. The proposed method is robust against various resolutions and orientations of objects at each view. Our system uses the fuzzy logic to employ a human-like color perception in its decision making stage in order to handle color inconstancy...