Motion segmentation-based surveillance video compression using adaptive particle swarm optimization

Sengar, Sandeep Singh; Mukhopadhyay, Susanta

doi:10.1007/s00521-019-04635-6

Cited by 31 publications

(15 citation statements)

References 58 publications

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“…The transmission is subsequently limited to the moving segments after compression using the wavelet transform-based compression approach. In [33], the authors proposed a surveillance video compression based on motion detection and segmentation, employing a JPEG-like chain to compress the data.…”

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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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: 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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“…The authors in [62] suggested a hybrid-segmentation method based on the optical flow adaptive threshold and the PSO variant to tackle the problem of object detection and tracking in video surveillance. PSO was used to determine the best matching block in the motion estimation within the search space.…”

Section: Image Segmentation Based On Genetic Algorithms (Ga)mentioning

confidence: 99%

Emerging Applications of Bio-Inspired Algorithms in Image Segmentation

2021

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Image processing is one example of digital media. It consists of a set of operations to handle an image. Image segmentation is among its main important operations. It involves dividing the image into several parts or regions to extract vital information or identify relevant objects. Many techniques of artificial intelligence, including bio-inspired algorithms, have been used in this regard. This article collected the state-of-the-art studies presenting image-segmentation techniques combined with four bio-inspired algorithms including particle swarm optimization (PSO), genetic algorithms (GA), ant colony optimization (ACO), and artificial bee colonies (ABC). This research work aimed at showing the importance of image segmentation and its combination with these algorithms. This article provides insights on how these algorithms are adapted to image-segmentation combinatorial problems, which assist researchers to start the first hands-on application. It also discusses their setting parameters and the highly used algorithms such as PSO, GA, ACO, and ABC. The article presents new research directions in image segmentation based on bio-inspired algorithms.

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“…Huffman coding is employed to encode I frame on the quantized version of DCT by itself. Moving objects and motion failures are detected by adaptive thresholding and differencing approach done by [32] along with Particle swarm optimization-based Motion estimation to remove temporal redundancy.…”

Section: Video Compression Essentialsmentioning

confidence: 99%

State-Of-The-Art In Video Processing: Compression, Optimization And Retrieval

Megala¹

2021

TURCOMAT

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Video compression plays a vital role in the modern social media networking with plethora of multimedia applications. It empowers transmission medium to competently transfer videos and enable resources to store the video efficiently. Nowadays high-resolution video data are transferred through the communication channel having high bit rate in order to send multiple compressed videos. There are many advances in transmission ability, efficient storage ways of these compressed video where compression is the primary task involved in multimedia services. This paper summarizes the compression standards, describes the main concepts involved in video coding. Video compression performs conversion of large raw bits of video sequence into a small compact one, achieving high compression ratio with good video perceptual quality. Removing redundant information is the main task in the video sequence compression. A survey on various block matching algorithms, quantization and entropy coding are focused. It is found that many of the methods having computational complexities needs improvement with optimization.

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Motion segmentation-based surveillance video compression using adaptive particle swarm optimization

Cited by 31 publications

References 58 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

Emerging Applications of Bio-Inspired Algorithms in Image Segmentation

State-Of-The-Art In Video Processing: Compression, Optimization And Retrieval

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