Moving Region Detection in Compressed Video

Töreyi̇n, Behçet Uğur; Çetin, A. Enis; Aksay, Anil; Akhan, M.B.

doi:10.1007/978-3-540-30182-0_39

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…Most approaches only exploit dc coefficients of the discrete cosine transformation (DCT) [26]- [30] to identify moving regions. So they can only obtain very rough silhouettes of moving objects at the block resolution, and cannot obtain object contours in pixel accuracy.…”

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confidence: 99%

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Modeling Background and Segmenting Moving Objects from Compressed Video

Wang

Yang

Gao

2008

IEEE Trans. Circuits Syst. Video Technol.

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Abstract-Modeling background and segmenting moving objects are significant techniques for video surveillance and other video processing applications. Most existing methods of modeling background and segmenting moving objects mainly operate in the spatial domain at pixel level. In this paper, we present three new algorithms (running average, median, mixture of Gaussians) modeling background directly from compressed video, and a two-stage segmentation approach based on the proposed background models. The proposed methods utilize discrete cosine transform (DCT) coefficients (including ac coefficients) at block level to represent background, and adapt the background by updating DCT coefficients. The proposed segmentation approach can extract foreground objects with pixel accuracy through a two-stage process. First a new background subtraction technique in the DCT domain is exploited to identify the block regions fully or partially occupied by foreground objects, and then pixels from these foreground blocks are further classified in the spatial domain. The experimental results show the proposed background modeling algorithms can achieve comparable accuracy to their counterparts in the spatial domain, and the associated segmentation scheme can visually generate good segmentation results with efficient computation. For instance, the computational cost of the proposed median and MoG algorithms are only 40.4% and 20.6% of their counterparts in the spatial domain for background construction.

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confidence: 99%

“…Based on the proposed background models, we present a two-stage segmentation approach to extract moving objects with pixel accuracy. Compared with [30], our segmentation approach based on background models has lower computational cost. Thus, our work gives a new solution that can efficiently and accurately process compressed surveillance video.…”

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confidence: 99%

Modeling Background and Segmenting Moving Objects from Compressed Video

Wang

Yang

Gao

2008

IEEE Trans. Circuits Syst. Video Technol.

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Object level frame comparison for video shot detection with orthogonal polynomials model

Krishnamoorthy¹,

Braveen²

2012

2012 International Conference on Emerging Trends in Science, Engineering and Technology (INCOSET)

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Vehicle License Plate Detection Using Only Block Partitioning Structure of the High Efficiency Video Coding (HEVC)

Beratoglu

Töreyi̇n

2019

2019 27th Signal Processing and Communications Applications Conference (SIU)

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In the field of video analytics, particularly traffic surveillance, there is a growing need for efficient and effective methods for processing and understanding video data. Traditional full video decoding techniques can be computationally intensive and time-consuming, leading researchers to explore alternative approaches in the compressed domain. This study introduces a novel random perturbation-based compressed domain method for reconstructing images from High Efficiency Video Coding (HEVC) bitstreams, specifically designed for traffic surveillance applications. To the best of our knowledge, our method is the first to propose substituting random perturbations for residual values, thereby creating a condensed representation of the original image while retaining information relevant to video understanding tasks, particularly focusing on vehicle detection and classification as key use cases.By not using any residual data, our proposed method significantly reduces the amount of data needed in the image reconstruction process, allowing for more efficient storage and transmission of information. This is particularly important when considering the vast amount of video data involved in surveillance applications. Applied to the public BIT-Vehicle dataset, we demonstrate a significant increase in the reconstruction speed compared to the traditional full decoding approach, with our proposed random perturbation-based method being approximately 56% faster than the pixel domain method. Additionally, we achieve a detection accuracy of 99.9%, on par with the pixel domain method, and a classification accuracy of 96.84%, only 0.98% lower than the pixel domain method. Furthermore, we showcase the significant reduction in data size, leading to more efficient storage and transmission. Our research establishes the potential of compressed domain methods in traffic surveillance applications, where speed and data size are critical factors. The study's findings can be extended to other object detection tasks, such as pedestrian detection, and future work may investigate the integration of compressed and pixel domain information, as well as the extension of these methods to the full video decoding process, encompassing both intra and inter encoded bitstreams.

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Moving Region Detection in Compressed Video

Cited by 3 publications

References 6 publications

Modeling Background and Segmenting Moving Objects from Compressed Video

Modeling Background and Segmenting Moving Objects from Compressed Video

Object level frame comparison for video shot detection with orthogonal polynomials model

Vehicle License Plate Detection Using Only Block Partitioning Structure of the High Efficiency Video Coding (HEVC)

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