Sharif Digital Repository

When an object or its background changes, occlusion or shape change occurs, most of the existed methods fail to track the target. To tackle this problem, we want to use sparse representation that has a great power in classification and reconstruction. Sparsity is a typical and practical hypothesis in many spaces. If a signal isn’t sparse in a space, it can be transformed to another space that is sparse in it. Articles that are published on visual tracking using sparse representation show that this field has attracted a lot of interest in the recent years. Here we have proposed two new methods that have reasonable results. Moreover, while it is well known that sparse representation-based... 

Most of current state-of-the-art methods for object tracking use adaptive tracking-by-detection. The performance of state-of-the-art methods is almost real-time with acceptable accuracy. These methods use tracking-by-detection because of its robustness. Tracking-bydetection methods use a detector as a tracker and sweep input for object of interest. They use their predictions to adapt their parameters and therefore be adaptive to appearance change in target. While suitable for cases when the object does not disappear from the scene, these methods tend to fail on occlusions. In this work, we build on a novel approach called Tracking-Learning-Detection (TLD) that overcomes this problem. In... 

To overcome challenges such as object appearance changes and environment illumination variations in tracking methods, online algorithms are suggested to be used instead of offline ones. Online algorithms update the model by the information acquired in the last processed frame. The main challenge of using online algorithms is the accumulation of small errors after several steps of updating of the model (drift) which disturbs the model and causes tracking failure. Using the object information in the first frame in each update can be considered as a solution. The proposed online semi-supervised boosting algorithms can overcome the drift problem at the expense of decreasing their capabilities in... 

Visual tracking is a classic problem, but is continuously an active area of research, in computer vision. Given a bounding box defining the object of interest (target) in the first frame of a video sequence, the goal of a general tracker is to determine the ob-ject’s bounding box in subsequent frames. Utilizing sparse representation, we propose a robust tracking algorithm to handle challenges such as illumination variation, pose change, and occlusion. Object appearance is modeled using a dictionary composed of target patch images contained in previous frames. In each frame, the target is found from a set of candidates via a likelihood measure that is proportional to the sum of the... 

Tracking is one of the old and still not thoroughly solved problems in machine vision. Its importance lies on its many applications. These applications vary from security surveillance to examining the motion pattern of atomic particles. There is not a tracker which has acceptable results in all situations, yet. A tracker faces many difficulties such as change in illumination and occlusion. In past, tracking was done by using filters or optical flows. By use of the advances in machine learning and pattern recognition, many models have been proposed to accomplish tracking by using these new learning methods. In this dissertation, we proposed a new tracking method which utilizes sparse...