Sökning: "multi- object tracking"
Visar resultat 1 - 5 av 25 avhandlingar innehållade orden multi- object tracking.
1. Conjugate priors for Bayesian object tracking
Sammanfattning : Object tracking refers to the problem of using noisy sensor measurements to determine the location and characteristics of objects of interest in clutter. Nowadays, object tracking has found applications in numerous research venues as well as application areas, including air traffic control, maritime navigation, remote sensing, intelligent video surveillance, and more recently environmental perception, which is a key enabling technology in autonomous vehicles. LÄS MER
2. Deep Learning For Model-Based Multi-Object Tracking
Sammanfattning : Multi-object tracking (MOT) is the task of estimating the state of multiple objects based on noisy sensor measurements. MOT is essential in various applications, such as pedestrian monitoring, vehicle tracking, animal behavior analysis, and others. LÄS MER
3. Poisson Multi-Bernoulli Mixtures for Multiple Object Tracking
Sammanfattning : Multi-object tracking (MOT) refers to the process of estimating object trajectories of interest based on sequences of noisy sensor measurements obtained from multiple sources. Nowadays, MOT has found applications in numerous areas, including, e.g. LÄS MER
4. Learning Object Properties From Manipulation for Manipulation
Sammanfattning : The world contains objects with various properties - rigid, granular, liquid, elastic or plastic. As humans, while interacting with the objects, we plan our manipulation by considering their properties. For instance, while holding a rigid object such as a brick, we adapt our grasp based on its centre of mass not to drop it. LÄS MER
5. Visual Object Tracking and Classification Using Multiple Sensor Measurements
Sammanfattning : Multiple sensor measurement has gained in popularity for computer vision tasks such as visual object tracking and visual pattern classification. The main idea is that multiple sensors may provide rich and redundant information, due to wide spatial or frequency coverage of the scene, which is advantageous over single sensor measurement in learning object model/feature and inferring target state/attribute in complex scenarios. LÄS MER