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Most existing vision-based methods for gaze tracking need a tedious calibration process. In this process, subjects are required to fixate on a specific point or several specific points in space. However, it is hard to cooperate, especially for children and human infants. In this paper, a new calibration-free gaze tracking system and method is presented for automatic measurement of visual acuity in...
At the current rate of technological advancement and social acceptance thereof, it will not be long before wearable devices will be common that constantly record the field of view of the user. We introduce a new database of image sequences, taken with a first person view camera, of realistic, everyday scenes. As a distinguishing feature, we manually transcribed the scene text of each image. This way,...
Visual odometry has been promoted as a fundamental component for intelligent vehicles. Relying solely on monocular image cues would be desirable. Nevertheless, this is a challenge especially in dynamically varying urban areas due to scale ambiguities, independent motions, and measurement noise. We propose to use probabilistic learning with auxiliar depth cues. Specifically, we developed an expert...
We use video metadata to perform activity detection from videos in the wild, particularly the TRECVID dataset. Unlike previous activity datasets (KTH, Weiz-mann, UCF sports, etc.), this test set is assembled from videos captured with a wide range of cameras, resulting in videos with different frame rates, audio/video bitrates, and resolutions. Because these measures correlate with the quality of the...
This paper presents a real-time framework for objects cursory recognition in cluster scene based on visual attention. First, multi-scale image features are combined into a single saliency map. Then, k-means method is used to estimate the position of objects from cluster scene by saliency map. Finally, we construct global color feature vector for saliency regions and recognize the objects by their...
We review some recent techniques for 3D tracking and occlusion handling for computer vision-based augmented reality. We discuss what their limits for real applications are, and why object recognition techniques are certainly the key to further improvements.
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