Sparse Representation Frameworks for Inference Problems in Visual Sensor Networks
Research Database - Sabanci University
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 fitswell 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.
A sparsity-driven action recognition method is proposed by casting the classification problem as an optimization problem. We solve the optimization problem by enforcing sparsity through l1 regularization and perform action recognition. We have demonstrated the superiority of our method when observations are low-resolution,occluded, and noisy. To the best of our knowledge, this is the first action recognition method that uses sparse representation. In addition, we have proposed an adaptation of this method for VSN resource constraints. We have also performed an analysis of the role of sparsity in classification for two different action recognition problems. We have proposed a feature compression framework for human tracking applications in visual sensor networks. In this framework, we perform decentralized tracking: each camera extracts useful features from the images it has observed and sends them to a fusion node which collects the multi-view image features and performs tracking. In tracking, extracting features usually results a likelihood function. To reduce communication in the network, we compress the likelihoods by first splitting them into blocks, and then transforming each block to a proper domain and taking only the most significant coefficients in this representation. To the best of our knowledge, compression of features computed in the context of tracking in a VSN has not been proposed in previous works. We have applied our method for indoor and outdoor tracking scenarios. Experimental results show that our approach can save up to 99.6% of the bandwidth compared to centralized approaches that compress raw images to decrease the communication. We have also shown that our approach outperforms existing decentralized approaches.
Furthermore, we have extended this tracking framework and proposed a sparsity-driven approach for human tracking in VSNs. We have designed special overcomplete dictionaries that exploit the specific known geometry of the measurement scenario and used these dictionaries for sparse representation of likelihoods. By obtaining dictionaries that match the structure of the likelihood functions, we can represent likelihoods with few coefficients, and thereby decrease the communication in the network. This is the first method in the literature that uses sparse representation to compress likelihood functions and applies this idea for VSNs. We have tested our approach for indoor and outdoor tracking scenarios and demonstrated that our approach can achieve bandwidth reduction better than our feature compression frame-work. We have also presented that our approach outperforms existing decentralized and distributed approaches.