Sökning: "Discriminative correlation filter"
Hittade 5 uppsatser innehållade orden Discriminative correlation filter.
1. Tracking Under Countermeasures Using Infrared Imagery
Master-uppsats, Linköpings universitet/DatorseendeSammanfattning : Object tracking can be done in numerous ways, where the goal is to track a target through all frames in a sequence. The ground truth bounding box is used to initialize the object tracking algorithm. Object tracking can be carried out on infrared imagery suitable for military applications to execute tracking even without illumination. LÄS MER
2. Visual Tracking with Deep Learning : Automatic tracking of farm animals
Master-uppsats, KTH/Radio Systems Laboratory (RS Lab)Sammanfattning : Automatic tracking and video of surveillance on a farm could help to support farm management. In this project, an automated detection system is used to detect sows in surveillance videos. This system is based upon deep learning and computer vision methods. LÄS MER
3. Visual Tracking with Deformable Continuous Convolution Operators
Master-uppsats, Linköpings universitet/DatorseendeSammanfattning : Visual Object Tracking is the computer vision problem of estimating a target trajectory in a video given only its initial state. A visual tracker often acts as a component in the intelligent vision systems seen in for instance surveillance, autonomous vehicles or robots, and unmanned aerial vehicles. LÄS MER
4. Improving Discriminative Correlation Filters for Visual Tracking
Master-uppsats, Linköpings universitet/DatorseendeSammanfattning : Generic visual tracking is one of the classical problems in computer vision. In this problem, no prior knowledge of the target is available aside from a bounding box in the initial frame of the sequence. LÄS MER
5. Determining Attribute Importance Using an Ensemble of Genetic Programs and Permutation Tests : Relevansbestämning av attribut med hjälp av genetiska program och permutationstester
Master-uppsats, KTH/Skolan för datavetenskap och kommunikation (CSC)Sammanfattning : When classifying high-dimensional data, a lot can be gained, in terms of both computational time and precision, by only considering the most important features. Many feature selection methods are based on the assumption that important features are highly correlated with their corresponding classes, but mainly uncorrelated with each other. LÄS MER