Detecting and segmenting salient objects from natural scenes, often referred to as salient
object detection, has attracted great interest in computer vision. While many models have …

There is a large variety of trackers, which have been proposed in the literature during the
last two decades with some mixed success. Object tracking in realistic scenarios is a difficult …

We extensively compare, qualitatively and quantitatively, 41 state-of-the-art models (29
salient object detection, 10 fixation prediction, 1 objectness, and 1 baseline) over seven …

Visual saliency analysis detects salient regions/objects that attract human attention in
natural scenes. It has attracted intensive research in different fields such as computer vision …

This work presents an unsupervised learning based approach to the ubiquitous computer
vision problem of image matching. We start from the insight that the problem of frame …

The mean-shift procedure is a popular object tracking algorithm since it is fast, easy to
implement and performs well in a range of conditions. We address the problem of scale …

The performance of single cue object tracking algorithms may degrade due to complex
nature of visual world and environment challenges. In recent past, multicue object tracking …

Mean-Shift tracking is a popular algorithm for object tracking since it is easy to implement
and it is fast and robust. In this paper, we address the problem of scale adaptation of the …

Smart surveillance systems are used to monitor specific areas, such as homes, buildings,
and borders, and these systems can effectively detect any threats. In this work, we …

Visual representation is crucial for visual tracking method׳ s performances. Conventionally,
visual representations adopted in visual tracking rely on hand-crafted computer vision …