Practical Matters in Computer Vision

Jain, Lakhmi C.; Favorskaya, Margarita N.

doi:10.1007/978-3-319-11430-9_1

Cited by 2 publications

(1 citation statement)

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“…Ensuring humans' security, either in public or private spaces, is becoming a major priority for all nations. This issue arises the need for video surveillance systems which consist basically in objects detection, objects tracking and behavior understanding (Jain and Favorskaya, 2015). The most important task in surveillance systems is moving objects detection.…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal Center-symmetric Local Derivative Patterns for Objects Detection in Video Surveillance

Jmal

Souidene

Attia

2016

Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications

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Nowadays, more attention is being focused on background subtraction methods regarding their importance in many computer vision applications. Most of the proposed approaches are classified as pixel-based due to their low complexity and processing speed. Other methods are considered as spatiotemporal-based as they consider the surroundings of each analyzed pixel. In this context, we propose a new texture descriptor that is suitable for this task. We benefit from the advantages of local binary patterns variants to introduce a novel spatiotemporal center-symmetric local derivative patterns (STCS-LDP). Several improvements and restrictions are set in the neighboring pixels comparison level, to make the descriptor less sensitive to noise while maintaining robustness to illumination changes. We also present a simple background subtraction algorithm which is based on our STCS-LDP descriptor. Experiments on multiple video sequences proved that our method is efficient and produces comparable results to the state of the art.

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Section: Introductionmentioning

confidence: 99%