Robust feature fusion for multi-view object tracking

摘要

Multi-Task Multi-View Tracking (MTMVT) is used to visually identify and track an object. The MTMVT employs visual cues such as color, edge, and texture as complementary features to intensity in the target appearance representation, and combines a multi-view representation with a robust multi-task learning to solve feature fusion tracking problems. To reduce computational demands, feature matrices are sparsely represented in a single matrix and then decomposed into a pair of matrices to improve robustness to outliers. Views and particles are further combined based on interdependency and commonality single computational task. Probabilities are computed for each particle across all features and the particle with the greatest probability is selected as the target tracking result.

主权项

1. A method for tracking an object using a sensor network comprising:
selecting one or multiple reference frames from a plurality of data frames captured from the sensor network; identifying the object to track and obtaining a plurality of tracking target templates from the reference frames; extracting a set of multiple views from each of the tracking target templates; sampling a plurality of image patches proximate the location of the object in subsequent frames relative to the reference frame; extracting the set of multiple views from each image patch; solving a minimization problem in a robust multi-view multi-task framework for each image patch to calculate a probability for each of the multiple views; calculating an entropy of each of the multiple views using the probabilities of all the image patches; and determining a tracking result using the image patch with the highest probability using a multi-view weighting for the purpose of tracking and identifying the object.