Compressive Sensing for Target Detection and Tracking within Wireless Visual Sensor Networks-based Surveillance applications

Wireless Visual Sensor Networks (WVSNs) have gained significant importance in the last few years and have emerged in several distinctive applications. The main aim of this research is to investigate the use of adaptive Compressive Sensing (CS) for efficient target detection and tracking in WVSN-based surveillance applications. CS is expected to overcome the WVSN resource constraints such as memory limitation, communication bandwidth and battery constraints. In addition, adaptive CS dynamically chooses variable compression rates according to different data sets to represent captured images in an efficient way hence saving energy and memory space. In this work, a literature review on compressive sensing, target detection and tracking for WVSN is carried out to investigate existing techniques. Only single view target tracking is considered to keep minimum number of visual sensor nodes in a wake-up state to optimize the use of nodes and save battery life which is limited in WVSNs. To reduce the size of captured images an adaptive block CS technique is proposed and implemented to compress the high volume data images before being transmitted through the wireless channel. The proposed technique divides the image to blocks and adaptively chooses the compression rate for relative blocks containing the target according to the sparsity nature of images. At the receiver side, the compressed image is then reconstructed and target detection and tracking are performed to investigate the effect of CS on the tracking performance. Least mean square adaptive filter is used to predicts target’s next location, an iterative quantized clipped LMS technique is proposed and compared with other variants of LMS and results have shown that it achieved lower error rates than other variants of lMS. The tracking is performed in both indoor and outdoor environments for single/multi targets. Results have shown that with adaptive block compressive sensing (CS) up to 31% measurements of data are required to be transmitted for less sparse images and 15% for more sparse, while preserving the 33dB image quality and the required detection and tracking performance. Adaptive CS resulted in 82% energy saving as compared to transmitting the required image with no C

A Hybrid Adaptive Compressive Sensing Model for Visual Tracking in Wireless Visual Sensor Networks

The employ of Wireless Visual Sensor Networks (WVSNs) has grown enormously in the last few years and have emerged in distinctive applications. WVSNs-based Surveillance applications are one of the important applications that requires high detection reliability and robust tracking, while minimizing the usage of energy to maximize the lifetime of sensor nodes as visual sensor nodes can be left for months without any human interaction. The constraints of WVSNs such as resource constraints due to limited battery power, memory space and communication bandwidth have brought new WVSNs implementation challenges. Hence, the aim of this paper is to investigate the impact of adaptive Compressive Sensing (CS) in designing efficient target detection and tracking techniques, to reduce the size of transmitted data without compromising the tracking performance as well as space and energy constraints. In this paper, a new hybrid adaptive compressive sensing scheme is introduced to dynamically achieve higher compression rates, as different datasets have different sparsity nature that affects the compression. Afterwards, a modified quantized clipped Least Mean square (LMS) adaptive filter is proposed for the tracking model. Experimental results showed that adaptive CS achieved high compression rates reaching 70%, while preserving the detection and tracking accuracy which is measured in terms of mean squared error, peak-signal-to-noise-ratio and tracking trajectory

Analytical framework for Adaptive Compressive Sensing for Target Detection within Wireless Visual Sensor Networks

Wireless visual sensor networks (WVSNs) are composed of a large number of visual sensor nodes covering a specifc geographical region. This paper addresses the target detection problem within WVSNs where visual sensor nodes are left unattended for long-term deployment. As battery energy is a critical issue it is always challenging to maximize the network's lifetime. In order to reduce energy consumption, nodes undergo cycles of active-sleep periods that save their battery energy by switching sensor nodes ON and OFF, according to predefined duty cycles. Moreover, adaptive compressive sensing is expected to dynamically reduce the size of transmitted data through the wireless channel, saving communication bandwidth and consequently saving energy. This paper derives for the first time an analytical framework for selecting node's duty cycles and dynamically choosing the appropriate compression rates for the captured images and videos based on their sparsity nature. This reduces energy waste by reaching the maximum compression rate for each dataset without compromising the probability of detection. Experiments were conducted on different standard datasets resembling different scenes; indoor and outdoor, for single and multiple targets detection. Moreover, datasets were chosen with different sparsity levels to investigate the effect of sparsity on the compression rates. Results showed that by selecting duty cycles and dynamically choosing the appropriate compression rates, the desired performanc

A Hybrid Adaptive Compressive Sensing Model for Visual Tracking in Wireless Visual Sensor Networks

The employ of Wireless Visual Sensor Networks (WVSNs) has grown enormously in the last few years and have emerged in distinctive applications. WVSNs-based Surveillance applications are one of the important applications that requires high detection reliability and robust tracking, while minimizing the usage of energy to maximize the lifetime of sensor nodes as visual sensor nodes can be left for months without any human interaction. The constraints of WVSNs such as resource constraints due to limited battery power, memory space and communication bandwidth have brought new WVSNs implementation challenges. Hence, the aim of this paper is to investigate the impact of adaptive compressive Sensing (CS) in designing efficient target detection and tracking techniques, to reduce the size of transmitted data without compromising the tracking performance as well as space and energy constraints. In this paper, a new hybrid adaptive compressive sensing scheme is introduced to dynamically achieve higher compression rates, as different datasets have different sparsity nature that affects the compression. Afterwards, a modified quantized clipped Least Mean square (LMS) adaptive filter is proposed for the tracking model. Experimental results showed that adaptive CS achieved high compression rates reaching 70%, while preserving the detection and tracking accuracy which is measured in terms of mean squared error, peak-signal-to-noise-ratio and tracking trajectory

Compressive Sensing-based Target Tracking for Wireless Visual Sensor Networks

Limited storage, channel bandwidth, and battery lifetime are the main concerns when dealing with Wireless Visual Sensor Networks (WVSNs). Surveillance application for WVSNs is one of the important applications that requires high detection reliability and robust tracking, while minimizing the usage of energy as visual sensor nodes can be left for months without any human interaction. In surveillance applications, within WVSN, only single view target tracking is achieved to keep minimum number of visual sensor nodes in a ’wake-up’ state to optimize the use of nodes and save battery life time, which is limited in WVSNs. Least Mean square (LMS) adaptive filter is used for tracking to estimate target’s next location. Moreover, WVSNs retrieve large data sets such as video, and still images from the environment requiring high storage and high bandwidth for transmission which are limited. Hence, suitable representation of data is needed to achieve energy efficient wireless transmission and minimum storage. In this paper, the impact of CS is investigated in designing target detection and tracking techniques for WVSNs- based surveillance applications, without compromising the energy constraint which is one of the main characteristics of WVSNs. Results have shown that with compressive sensing (CS) up to 31 % measurements of data are required to be transmitted, while preserving the detection and tracking accuracy which is measured through comparing targets trajectory tracking

A Hybrid Adaptive Compressive Sensing Model for Visual Tracking in Wireless Visual Sensor Networks

The employ of Wireless Visual Sensor Networks (WVSNs) has grown enormously in the last few years and have emerged in distinctive applications. WVSNs-based Surveillance applications are one of the important applications that requires high detection reliability and robust tracking, while minimizing the usage of energy to maximize the lifetime of sensor nodes as visual sensor nodes can be left for months without any human interaction. The constraints of WVSNs such as resource constraints due to limited battery power, memory space and communication bandwidth have brought new WVSNs implementation challenges. Hence, the aim of this paper is to investigate the impact of adaptive Compressive Sensing (CS) in designing efficient target detection and tracking techniques, to reduce the size of transmitted data without compromising the tracking performance as well as space and energy constraints. In this paper, a new hybrid adaptive compressive sensing scheme is introduced to dynamically achieve higher compression rates, as different datasets have different sparsity nature that affects the compression. Afterwards, a modified quantized clipped Least Mean square (LMS) adaptive filter is proposed for the tracking model. Experimental results showed that adaptive CS achieved high compression rates reaching 70%, while preserving the detection and tracking accuracy which is measured in terms of mean squared error, peak-signal-to-noise-ratio and tracking trajectory

Comparative Analysis on the Competitiveness of Conventional and Compressive Sensing-based Query Processing

Optimization of the lifetime of the battery within wireless sensor networks (WSNs) is challenging due to communication infrastructure. Subsequently, minimizing the amount of power required for data collection and processing to serve the intended purposes has become an open research problem. Conventional and compressive sensing-based (CS) query processing being the candidates to perform these tasks, require a comparative analysis in the current WSN application context. In this paper. Simulations have been carried out to compare the performance of conventional and compressive sensing-based (CS) query processing with respect to energy efficiency, sensing reliability and normalized estimation error within WSN. A significant reduction in the computational complexity reaching 70% is noticed using CS compared to conventional query processing algorithms. Moreover, it is observed that up to 90% sensing reliability can be achieved with CS compared to existing query processing. Hence, the reduction in computational complexity has not compromised the sensing reliability with an observed reduction in the normalized estimation error

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

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

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

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