Journal ArticleDOI
Background-Modeling-Based Adaptive Prediction for Surveillance Video Coding
TLDR
A background-modeling-based adaptive prediction (BMAP) method that can achieve at least twice the compression ratio on surveillance videos as AVC (MPEG-4 Advanced Video Coding) high profile, yet with a slightly additional encoding complexity.Abstract:
The exponential growth of surveillance videos presents an unprecedented challenge for high-efficiency surveillance video coding technology. Compared with the existing coding standards that were basically developed for generic videos, surveillance video coding should be designed to make the best use of the special characteristics of surveillance videos (e.g., relative static background). To do so, this paper first conducts two analyses on how to improve the background and foreground prediction efficiencies in surveillance video coding. Following the analysis results, we propose a background-modeling-based adaptive prediction (BMAP) method. In this method, all blocks to be encoded are firstly classified into three categories. Then, according to the category of each block, two novel inter predictions are selectively utilized, namely, the background reference prediction (BRP) that uses the background modeled from the original input frames as the long-term reference and the background difference prediction (BDP) that predicts the current data in the background difference domain. For background blocks, the BRP can effectively improve the prediction efficiency using the higher quality background as the reference; whereas for foreground-background-hybrid blocks, the BDP can provide a better reference after subtracting its background pixels. Experimental results show that the BMAP can achieve at least twice the compression ratio on surveillance videos as AVC (MPEG-4 Advanced Video Coding) high profile, yet with a slightly additional encoding complexity. Moreover, for the foreground coding performance, which is crucial to the subjective quality of moving objects in surveillance videos, BMAP also obtains remarkable gains over several state-of-the-art methods.read more
Citations
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Journal ArticleDOI
Background Subtraction in Real Applications: Challenges, Current Models and Future Directions
TL;DR: In this paper, a survey of background subtraction methods used in real applications is presented, in order to identify the real challenges met in practice, the current used background models and to provide future directions.
Posted Content
Background Subtraction in Real Applications: Challenges, Current Models and Future Directions
TL;DR: This work identifies the background models that are effectively used in real applications that used background subtraction in order to identify the real challenges met in practice, the current used background models and to provide future directions.
Journal ArticleDOI
On the role and the importance of features for background modeling and foreground detection
Thierry Bouwmans,Caroline Silva,Cristina Marghes,Mohammed Sami Zitouni,Harish Bhaskar,Carl Frélicot +5 more
TL;DR: In this article, a comprehensive survey of features used within background modeling and foreground detection is presented, and a preliminary review of the key characteristics of features based on the types and sizes is provided in addition to investigating their intrinsic spectral, spatial and temporal properties.
Journal ArticleDOI
An Enhanced Coding Algorithm for Efficient Video Coding
TL;DR: The proposed algorithm made sufficient modification in the traditional run length coding algorithm by encoding the frames and removing the redundancies using the texture information similarity in the surveillance video, thereby achieved a better compression rate of 50% for a huge dataset of surveillance videos when compared to existing methodologies.
Journal ArticleDOI
Improving Description-Based Person Re-Identification by Multi-Granularity Image-Text Alignments
TL;DR: A Multi-granularity Image-text Alignments (MIA) model is proposed to alleviate the cross-modal fine-grained problem for better similarity evaluation in description-based person Re-id and obtains the state-of-the-art performance on the CUHK-PEDES dataset.
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