Abstract
In recent days, video analytics has become an inevitable research area in surveillance video due to the surveillance cameras placed everywhere and thus increasing video data in megabytes. Therefore, it is difficult to analyze the significant event in the video. To detect the important event in a video, there is a need to represent the video content with spatio-temporal features. As the spatio-temporal representative feature can describe both appearance and motion of the event. This paper introduces a novel technique to effectively achieve event detection. Since the shape and motion of the foreground object are usually balanced with each other, this paper uses the joint spatio-temporal representation using two novel features such as SI-HOG and TI-EOF. The SI-HOG feature captures the shape information of the foreground object with its structural information and the TI-EOF feature efficiently extracts the foreground object’s motion. To effectively find the video event, the Bayesian-based Markov Chain Inference Model is being introduced as the data description technique. The proposed work is implemented by using the MATLAB tool and the performance of the proposed work is evaluated by the following existing approaches such as MAC, OCELM, and DAML. The efficiency of the proposed approach is tested on UCSD ped1, UCSD ped2, and UMN datasets. The proposed method achieved high accuracy compared to existing methods in terms of event detection.
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Pandian, A.A., Maheswari, S. Joint Spatio-temporal representation based efficient video event detection using and BMCIM model. Multimed Tools Appl 82, 44577–44589 (2023). https://doi.org/10.1007/s11042-023-15055-z
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DOI: https://doi.org/10.1007/s11042-023-15055-z