Efficient compressed sensing based object detection system for video surveillance application in WMSN

Nandhini, S. Aasha; Radha, S.; Kishore, Rajalekshmi

doi:10.1007/s11042-017-4345-2

Cited by 11 publications

(12 citation statements)

References 8 publications

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“…The suggested approach for image processing and communication requires relatively little energy, as evidenced by practical test and simulation results. To save transmission energy, Nandhini et al [25] propose a method for detecting objects with fewer measures that combines a mean measurement differencing approach with an adaptive threshold strategy. CS-based background subtraction is measured based on the node before object information is sent, reducing complexity in terms of power, storage, and bandwidth.…”

Section: ) Compressed Data: According To Robust Primarymentioning

confidence: 99%

“…The DWT-based CS object identification framework [27] uses a simple measurement matrix termed the deadweight tonnage block diagonal matrix to refine the pixel-based foreground following the block-based foreground recognition phase in the first stage. The averaging approach using the Adaptive Threshold Technology (MMDATS) in [25] is based on the framework for robust subspace learning. The OMP approach is used to reconstruct the object from foreground measurements.…”

Section: ) Compressed Data: According To Robust Primarymentioning

confidence: 99%

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Moving Object Detection over Wireless Visual Sensor Networks using Spectral Dual Mode Background Subtraction

AbdelTawab¹,

Abdelhalim²,

Habib³

2022

IJACSA

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Wireless Visual Sensor Networks (WVSN) play an essential role in tracking moving objects. WVSN's key drawbacks are storage, power, and bandwidth. Background subtraction is used in the early stages of target tracking to extract moving targets from video images. Many standard methods of subtracting backgrounds are no longer suitable for embedded devices because they use complex statistical models to manage small changes in lighting. This paper introduces a system based on the Partial Discrete Cosine Transform (PDCT), reducing the vast dimensions of processed data while retaining most of the important information, thereby reducing processing and transmission energy. It also uses a dual-mode single Gaussian model (SGM) for accurate detection of moving objects. The proposed system's performance is to be assessed using the standard CDnet 2014 benchmark dataset in terms of detection accuracy and time complexity. Furthermore, the suggested method is compared to previous WVSN background subtraction methods. Simulation results show that the proposed method consistently has 15% better accuracy and is up to 3 times faster than the state-of-the-art object detection methods for WVSN. Finally, we showed the practicality of the suggested method by simulating it in a sensor network environment using the Contiki OS Cooja Simulator and implementing it in a real testbed using Cortex M3 open nodes of IOT-LAB.

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Section: ) Compressed Data: According To Robust Primarymentioning

confidence: 99%

Section: ) Compressed Data: According To Robust Primarymentioning

confidence: 99%

Moving Object Detection over Wireless Visual Sensor Networks using Spectral Dual Mode Background Subtraction

AbdelTawab¹,

Abdelhalim²,

Habib³

2022

IJACSA

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“…Due to the characteristics of the CS imaging method [4], the original signal can be sampled without digital conversion and storage, thus the complexity of sampling calculation can be greatly reduced, the hardware requirements of the sampling equipment can be simplified, and the sampling rate can be improved. This makes the CS method have unique advantages in fields such as video compression [5,6], distributed coding [7], sensor networks [8], radar imaging [9], medical imaging [10], etc.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Rate Blocked Compressive Sensing Method for Video

Wang

Chen

2021

Entropy

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An adaptive rate Compressive Sensing (CS) method for video signals is proposed. The Blocked Compressive Sensing (BCS) scheme is adopted in this method. Firstly, each video frame is blocked and measured by the BCS scheme, and then the mean and variance of each image block are estimated by observing the CS measurement results. Using the mean and variance of each image block, the sparsity of the block is estimated and then the block can be classified. Adaptive rate sampling is realized by assigning different sampling rates to different classes. At the same time, in order to make better use of the correlation between video frames, a reference block subtraction method is also designed in this paper, which uses the estimates of the sparsity of image blocks as the basis for the reference block update. All operations of the proposed method only depend on the CS measurement results of image blocks and all calculations are simple. Thus, the proposed method is suitable for implementation in CS sampling devices with limited computational performance. Experiment results show that, compared with the actual values, the sparsity estimates and block classification results of the proposed method are accurate. Compared with the latest adaptive Compressive Video Sensing methods, the reconstructed image quality of the proposed method is better.

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“…Examples of this line of thinking are presented in Refs. [16][17][18][19] with the variations in measurement matrices (e.g., Hybrid Matrix in Ref. [19]; Gaussian/BPBD Matrix in Ref.…”

Section: Introductionmentioning

confidence: 99%

“…[16][17][18][19] with the variations in measurement matrices (e.g., Hybrid Matrix in Ref. [19]; Gaussian/BPBD Matrix in Ref. [17]; the proposed Adaptive CS in Ref.…”

Section: Introductionmentioning

confidence: 99%

Low-Cost Approach for Improving Video Transmission Efficiency in WVSN

Liu

Deng

Tang³

et al. 2020

J. Shanghai Jiaotong Univ. (Sci.)

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The wireless visual sensor network (WVSN) as a new emerged intelligent visual system, has been applied in many video monitoring sites. However, there is still great challenge because of the limited wireless network bandwidth. To resolve the problem, we propose a real-time dynamic texture approach which can detect and reduce the temporal redundancy during many successive image frames. Firstly, an adaptively learning background model is improved to discover successive similar image frames from the inputting video sequence. Then, the dynamic texture model based on the singular value decomposition is adopted to distinguish foreground and background element dynamics. Furthermore, a background discarding strategy based on visual motion coherence is proposed to determine whether each image frame is streamed or not. To evaluate the trade-off performance of the proposed method, it is tested on the CDW-2014 dataset, which can accurately detect the first foreground frame when the moving objects of interest appear in the field of view in the most tested dynamic scenes, and the misdetection rate of the undetected foreground frames is near to zero. Compared to the original stream, it can reduce the occupied bandwidth a lot and its computational cost is relatively lower than the state-of-the-art methods.

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Efficient compressed sensing based object detection system for video surveillance application in WMSN

Cited by 11 publications

References 8 publications

Moving Object Detection over Wireless Visual Sensor Networks using Spectral Dual Mode Background Subtraction

Moving Object Detection over Wireless Visual Sensor Networks using Spectral Dual Mode Background Subtraction

An Adaptive Rate Blocked Compressive Sensing Method for Video

Low-Cost Approach for Improving Video Transmission Efficiency in WVSN

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