Adaptive compressive sensing for target tracking within wireless visual sensor networks-based surveillance applications

Abstract

Wireless Visual Sensor Networks (WVSNs) have gained significant importance in the last few years and have emerged in several distinctive applications. The main aim is to design low power WVSN surveillance application using adaptive Compressive Sensing (CS) which is expected to overcome the WVSN resource constraints such as memory limitation, communication bandwidth and battery constraints. In this paper, an adaptive block CS technique is proposed and implemented to represent the high volume of captured images in a way for energy efficient wireless transmission and minimum storage. Furthermore, to achieve energy-efficient target detection and tracking with high detection reliability and robust tracking, to maximize the lifetime of sensor nodes as they can be left for months without any human interactions. Adaptive CS is expected to dynamically achieve higher compression rates depending on the sparsity nature of different datasets, while only compressing relative blocks in the image that contain the target to be tracked instead of compressing the whole image. Hence, saving power and increasing compression rates. Least mean square adaptive filter is used to predicts target's next location to investigate the effect of CS on the tracking performance. The tracking is achieved in both indoor and outdoor environments for single/multi targets. Results have shown that with adaptive block CS up to 20% measurements of data are required to be transmitted while preserving the required performance for target detection and tracking.

Modified Block Compressed Sensing for Extraction of Fetal Electrocardiogram from Mother Electrocardiogram Using Block Compressed Sensing Based Guided FOCUSS and FAST-Independent Component

“…PSNR, SINR and correlation, which clearly shows that proposed method outperforms the BSBL-BO, Rakness, BCS-SPL, BCS-DWT and BCS-DCT algorithms. In BCS, FECG signals are distributed into k×k blocks that are sampled using sequential walsh-hadmard matrix [9].…”

Analytical framework for adaptive compressive sensing for target detection within wireless visual sensor networks

“…In [20], an adaptive block CS technique is proposed and implemented to represent the high volume of captured images for the purpose of energy efficient wireless transmission and minimum storage. Adaptive CS is expected to dynamically achieve higher compression rates depending on the sparsity nature of different datasets, while only compressing relative blocks in the image that contain the target to be tracked instead of compressing the whole image.…”

“…This is accomplished by analyzing the integration of selecting node's duty cycles using the probability density functions introduced in [8] and dynamically choosing the appropriate compression rates for captured images and videos, which are expected to reduce energy waste by reaching the maximum compression rate for each dataset without compromising the probability of detection. The adaptive compressive sensing scheme introduced in [20] proved that for a specific sparsity level a corresponding compression rate is required to guarantee image reconstruction with minimum mean square error (MSE) and approximately 33dB Peak signal to noise ratio (PSNR). Therefore, the aim is to derive an analytical framework integrating adaptive CS to the detection problem and considering the resource constraints within WVSNs for target detection to evaluate the impact of energy saving due to visual sensor nodes' duty cycles.…”

Energy‐efficient compressive sensing for multi‐target tracking in wireless visual sensor networks

“…In Ghazalian et al, 20,21 the quality of experience (QoE) constraint includes the target coverage and quality of the captured targets' images for minimizing the energy consumption by selection of the proper visual sensors. In Aasha Nandhini and Radha 24 and Fayed et al, 25 the application of compressive sensing as an easy process with low energy consumption is stated for target tracking in visual sensor networks without any sensor selection for more energy saving. In fact, in this paper, the problem of minimizing the energy consumption is solved, while the quality of experience (QoE) and accuracy of the compressive sensing constraints are satisfied by selection of the suitable visual sensors and setting their parameters properly.…”