Automatic video activity detection using compressed domain motion trajectories for H.264 videos

Liu, Haowei; Sun, Ming–Ting; Wu, Ruei-Cheng; Yu, Shiaw-Shian

doi:10.1016/j.jvcir.2011.03.010

Cited by 15 publications

(8 citation statements)

References 9 publications

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“…In a video surveillance systems, data are most likely compressed. Therefore, there is a direction that extracts features from compressed domain, such as [102,129,130,67,108,118].…”

Section: Extracting Features From Video Compressed Domainmentioning

confidence: 99%

Automatic Event Detection for Signal-based Surveillance

Xu,

Fookes,

Sridharan

2016

Preprint

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Signal-based Surveillance systems such as Closed Circuits Televisions (CCTV) have been widely installed in public places. Those systems are normally used to find the events with security interest, and play a significant role in public safety. Though such systems are still heavily reliant on human labour to monitor the captured information, there have been a number of automatic techniques proposed to analysing the data. This article provides an overview of automatic surveillance event detection techniques . Despite it's popularity in research, it is still too challenging a problem to be realised in a real world deployment. The challenges come from not only the detection techniques such as signal processing and machine learning, but also the experimental design with factors such as data collection, evaluation protocols, and ground-truth annotation. Finally, this article propose that multi-disciplinary research is the path towards a solution to this problem.

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“…In a video surveillance systems, data are most likely compressed. Therefore, there is a direction that extracts features from compressed domain, such as [102,129,130,67,108,118].…”

Section: Extracting Features From Video Compressed Domainmentioning

confidence: 99%

Automatic Event Detection for Signal-based Surveillance

Xu,

Fookes,

Sridharan

2016

Preprint

View full text Add to dashboard Cite

show abstract

“…In [10–12], Liu et al , Masmoudi et al and Ng and Chua present another category of approaches which relies on the learning and classification of trajectories and events of cars. A feature vector is extracted for each trajectory of an object in motion and the system detects the vacancy or occupancy based on a classification.…”

Section: Introductionmentioning

confidence: 99%

Trajectory analysis for parking lot vacancy detection system

Masmoudi

Wali

Jamoussi

et al. 2016

IET Intelligent Transport Systems

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“…Chi et al [16] determine ROI regions through a visual rhythm feature as user attention model. Liu et al [17] investigate the event detection in a compressed domain automatically. After motion trajec-tories are extracted, they use prediction residuals to improve the robustness of the proposed video activity detection strategy.…”

Section: Introductionmentioning

confidence: 99%

Region-of-interest determination and bit-rate conversion for H.264 video transcoding

Huang

Chen

Tai

et al. 2013

EURASIP J. Adv. Signal Process.

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This paper presents a video bit-rate transcoder for baseline profile in H.264/AVC standard to fit the available channel bandwidth for the client when transmitting video bit-streams via communication channels. To maintain visual quality for low bit-rate video efficiently, this study analyzes the decoded information in the transcoder and proposes a Bayesian theorem-based region-of-interest (ROI) determination algorithm. In addition, a curve fitting scheme is employed to find the models of video bit-rate conversion. The transcoded video will conform to the target bit-rate by re-quantization according to our proposed models. After integrating the ROI detection method and the bit-rate transcoding models, the ROI-based transcoder allocates more coding bits to ROI regions and reduces the complexity of the re-encoding procedure for non-ROI regions. Hence, it not only keeps the coding quality but improves the efficiency of the video transcoding for low target bit-rates and makes the real-time transcoding more practical. Experimental results show that the proposed framework gets significantly better visual quality.

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Automatic video activity detection using compressed domain motion trajectories for H.264 videos

Cited by 15 publications

References 9 publications

Automatic Event Detection for Signal-based Surveillance

Automatic Event Detection for Signal-based Surveillance

Trajectory analysis for parking lot vacancy detection system

Region-of-interest determination and bit-rate conversion for H.264 video transcoding

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