PhD Defense: "UAV Sensor Fusion with Conditional Random Fields"

Amir Mohaymen Rahimi

December 3rd (Thursday), 3:30pm
Harold Frank Hall, Room 4164 (ECE Conf. Rm.)

Visual and multimodal sensors are playing an increasingly important role in applications such as object recognition, activity analysis, agriculture monitoring, autonomous navigations and assisted healthcare. Typical features computed from the raw data/measurements have significant mutual information that can be exploited towards improving the overall pattern recognition performance. In this dissertation we introduce principled methods to aggregate such information using conditional random field (CRF) models. Our aggregation is built on top of a large number of classifiers working independently on features computed from the raw data. The statistical dependencies among classifier outputs are formulated in a factorized form to characterize each class label. In the first part of the presentation we demonstrate the effectiveness of this approach in combining information from a large number of visual features in improving classification accuracy. Our experimental results show a significant improvement over the state-of-the-art aggregation methods on diverse image datasets. In the second application, we integrate the proposed CRF-based approach to combine multimodal measurements in a task involving a quadcopter UAV with autonomous path planning to detect and recognize the frontal view of a person. In this scenario, we utilize geographic location, time of day, view angle of the camera, altitude and magnetic heading, in addition to the video data, to estimate the body orientation in real time and automatically maneuver the UAV to maintain the frontal view of a person. The contextual information implicit in the multimodal measurements significantly reduce the vision-based computations for such real-time tasks.

About Amir Mohaymen Rahimi:

photo of amir mohaymen rahimi.jpg Amir is PhD student in the Department of Electrical and Computer Engineering at UCSB. He did his undergraduate at Schreyers honors college of Penn State University in Electrical and Computer Engineering and completed his masters at UCSB. During his time at UCSB, he has been a LEAPS fellow. He has interned at HRL Laboratories where he worked on object recognition using LIDAR data. His research interests are in vision-based pattern recognition, human drone interaction and autonomous UAV systems. Amir is also an Olympian athlete in fencing, a three-time Asian champion, a bronze medalist of Asian Olympic Games and a NCAA champion. He is also a certified pilot and a licensed sky-diving instructor.

Hosted by: Professor B. S. Manjunath