Sparse Representation Frameworks for Inference Problems in Visual Sensor Networks
Electronics Engineering, PhD Dissertation, 2013
Assoc. Prof. Müjdat Çetin (Thesis Supervisor), Assoc. Prof. Hakan Erdoğan, Assoc. Prof. Selim Balcısoy, Prof. Dr. Aytül Erçil, Asst. Prof. Ali Özer Ercan
Date &Time: November, 01st, 2013 - 16:45
Place: FENS 2019
Keywords : Visual sensor networks, camera networks, sparse representation, human tracking, compressing likelihoods functions, action recognition
Visual sensor networks (VSNs) form a new research area that merges computer vision and sensor networks. VSNs consist of small visual sensor nodes called camera nodes, which integrate an image sensor, an embedded processor, and a wireless transceiver. Having multiple cameras in a wireless network poses unique and challenging problems that do not exist either in computer vision or in sensor networks. Due to the resource constraints of the camera nodes, such as battery power and bandwidth, it is crucial to perform data processing and collaboration efficiently.
This thesis presents a number of sparse-representation based methods to be used in the context of surveillance tasks in VSNs. Performing surveillance tasks, such as tracking, recognition, etc., in a communication-constrained VSN environment is extremely challenging. Compressed sensing is a technique for acquiring and reconstructing a signal from small amount of measurements utilizing the prior knowledge that the signal has a sparse representation in a proper space. The ability of sparse representation tools to reconstruct signals from small amount of observations fits well with the limitations in VSNs for processing, communication, and collaboration. Hence, this thesis presents novel sparsity-driven methods that can be used in action recognition and human tracking applications in VSNs.