Dust particle detection in surveillance video using salient visual descriptors

•We identify the dust particles in images using salient visual descriptors.•We exploit localized color, texture and shape saliency features.•We develop a machine learning approach to detect dust particle in images.

Outdoor surveillance video that suffers from dirty camera lenses has the potential for deteriorated performance in many applications, such as vehicle tracking and target recognition. This paper proposes to identify the dust particles in images using a set of salient visual descriptors. More specifically, the proposed approach exploits an extended feature descriptor comprising localized color, texture and shape saliency features. These proposed features are further incorporated into a machine learning approach, followed by a dust particle localization approach, for detecting dust particle in images. The proposed approach is able to achieve superior dust particle detection performance to that of conventional approaches, as evaluated in real-world video surveillance scenarios.

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