An Energy-Efficient Wireless Video Sensor Node for Moving Object Surveillance

Ko, Jong Hwan; Mudassar, Burhan Ahmad; Mukhopadhyay, Saibal

doi:10.1109/tmscs.2015.2478469

Cited by 25 publications

(3 citation statements)

References 30 publications

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“…The frame difference method is of very low complexity and simple to implement, which makes it an appropriate choice to suit the constrained resources in a WSN. Meanwhile, it suffers from low region detection accuracy [31]. To overcome the low accuracy of pixel-based detection of the frame difference method, the blocks of the resulting difference are summed up to create an activity map that represents the level of the activity in each region.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…FD has been used for MOD and has presented advantages because of its low complexity, low memory, and processing speed. However, it has low accuracy when dealing with noisy backgrounds [31]. Edge Detection (ED) has also provided solutions to enhance the efficiency of the MOD algorithms, but it could suffer from high computational costs due to the edge detection operator's calculations.…”

Section: Background and Related Workmentioning

confidence: 99%

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An Efficient Low Complexity Region-of-Interest Detection for Video Coding in Wireless Visual Surveillance

et al. 2023

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Following its use in several applications, including video coding in wireless surveillance, moving object detection (MOD) has become a popular video analysis topic. Despite the considerable progress in the accuracy of MOD for video coding, its implementation in constrained sensors is a real challenge owing to their high complexity and energy consumption. Therefore, there is a great need to address the trade-off between the accuracy and the energy efficiency of MOD approaches for video coding in constrained systems. In this work, an energy-efficient region-of-interest (ROI) detection algorithm as a pre-encoder for wireless visual surveillance (WVS) is proposed. The algorithm ensures a trade-off between detection accuracy and computational complexity. To this end, we propose constructing an activity map by measuring each block activity between successive frames. The map scores are processed using a combination of a fast Gaussian smoother and a rank-order filter to improve accuracy. Only the blocks in motion are coded and counted for transmission. The accuracy of our approach has been evaluated on a large dataset using key performance metrics. It has been found that our algorithm outperforms other state-of-theart techniques in terms of true positive rate (TPR), with 80.84% on sensitivity metric, while exhibiting a well-balanced accuracy for all categories. A careful examination of the computational complexity confirms the low overhead. The energy and bitrate savings could achieve nearly 90% and 98%, respectively.

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Section: Proposed Methodsmentioning

confidence: 99%

Section: Background and Related Workmentioning

confidence: 99%

An Efficient Low Complexity Region-of-Interest Detection for Video Coding in Wireless Visual Surveillance

et al. 2023

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“…For a sensor platform design in this paper, we define the ROI as the region with moving objects, which is detected by a low-overhead method based on edge detection and frame differencing (ED+FD) [15]. In the original ED+FD method, the edge maps of each MB for two subsequent frames are compared, and the MBs with the sum of the pixel-wise difference (i.e.…”

Section: Baseline Roi-detection Enginementioning

confidence: 99%

An energy-efficient wireless video sensor node with a region-of-interest based multi-parameter rate controller for moving object surveillance

Mukhopadhyay

2016

2016 13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)

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This paper presents a lightweight video sensor node for moving object surveillance using region-of-interest (ROI) based coding and an on-line multi-parameter rate controller. The proposed ROI-based coding scheme determines ROI blocks, pre-processes non-ROI blocks using bit-truncation, and encodes all blocks using Motion JPEG. The on-line rate controller modulates the parameters of the ROI-based coding scheme to match the encoded data rate and transmission data rate under the variations in channel bandwidth and input video content. The low-complexity hardware of the ROI-based coding scheme reduces computation energy, and the on-line rate controller minimizes buffer requirement. The sensor node is designed in 130nm CMOS and prototyped in a Virtex-V FPGA. Simulations show that, under the same ROI quality, the proposed approach reduces system energy by 61% compared to H.264/AVC. Figure 1: ROI-based rate controller design (a) tied to H.264/AVC and (b) based on a simple encoder (MJPEG).

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Efficient scheduling of video camera sensor networks for IoT systems in smart cities

Naeem

Ejaz

Iqbal

et al. 2019

Trans Emerging Tel Tech

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Video camera sensor networks (VCSN) has numerous applications in smart cities, including vehicular networks, environmental monitoring, and smart houses. Scheduling of video camera sensor networks (VCSN) can reduce the computational complexity, increase energy efficiency, and enhance throughput for the Internet of things (IoT) systems. In this paper, we apply the iterative low‐complexity probabilistic evolutionary method for scheduling video cameras to maximize throughput in VCSNs for IoT systems. Scheduling of video cameras in VCSNs to maximize throughput is a combinatorial optimization problem whose computational complexity increases exponentially with the increase in the number of video cameras. We propose an iterative probabilistic method named as cross‐entropy optimization (CEO), which is an evolutionary algorithm. The combinatorial optimization problems can be solved using the CEO which is a generalized Monte Carlo technique. The proposed method updates its selected population (video cameras) at each iteration based on the Kullback Leibler (KL) distance/divergence. The KL distance/divergence is minimized using the probability distribution obtained from the learned from the group of selected samples of better solutions found in the previous iterations. The effectiveness of the CEO is verified in terms of optimality and simplicity through simulations. In addition, the results of the CEO are better than the suboptimal algorithms (ie, best norm‐based algorithm, genetic algorithm, and capacity upper‐bound–based greedy algorithm) and maximum of 2%‐3% deviation from the exhaustive search (optimal) with less complexity. The trade‐off between CEO and optimal is the computational complexity.

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An Energy-Efficient Wireless Video Sensor Node for Moving Object Surveillance

Cited by 25 publications

References 30 publications

An Efficient Low Complexity Region-of-Interest Detection for Video Coding in Wireless Visual Surveillance

An Efficient Low Complexity Region-of-Interest Detection for Video Coding in Wireless Visual Surveillance

An energy-efficient wireless video sensor node with a region-of-interest based multi-parameter rate controller for moving object surveillance

Efficient scheduling of video camera sensor networks for IoT systems in smart cities

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