Details

Autor(en) / Beteiligte

• Singha, Anu
• Bhowmik, Mrinal Kanti

Titel

Salient Features for Moving Object Detection in Adverse Weather Conditions During Night Time

Ist Teil von

• IEEE transactions on circuits and systems for video technology, 2020-10, Vol.30 (10), p.3317-3331

Ort / Verlag

New York: IEEE

Erscheinungsjahr

2020

Quelle

IEEE Electronic Library Online

Beschreibungen/Notizen

• Foreground segmentation of moving objects in adverse atmospheric conditions such as fog, rain, low light, and dust is a challenging task in computer vision. The advantages of thermal infrared imaging at night time under adverse atmospheric conditions have been demonstrated, which are due to the long wavelength. However, the existing state-of-the-art object detection techniques have not been useful in such scenarios. In this paper, we propose an improved background model that utilizes both thermal pixel intensity features and spatial video salient features. The proposed spatial video salient features are represented as an Akin-based per-pixel Boolean string over a local region block, and depend on the effect of neighboring pixels on a center pixel. The result of this Boolean procedure is referred to as the- Akin-Based Local Whitening Boolean Pattern (ALWBP), which differentiates foreground and background region accurately, even against a cluttered background. The background model is controlled via 1) the automatic adaptation of parameters such as the decision threshold <inline-formula> <tex-math notation="LaTeX">\text{R}_{\mathrm {T}} </tex-math></inline-formula> and, learning parameter L, and 2) the updating of background samples <inline-formula> <tex-math notation="LaTeX">\text{B}_{\mathrm {sample\_{}int}} </tex-math></inline-formula> and,- <inline-formula> <tex-math notation="LaTeX">\text{B}_{\mathrm {sample\_{}ALWBP}} </tex-math></inline-formula> to minimize 1) the effect of the background dynamics of outdoor scenes and 2) the temperature polarity changes during the maiden appearance of a moving object in thermal frame sequences. The performance of this model is evaluated using nine existing standard segmentation performance metrics on our newly created- Tripura University Video Dataset at Night Time (TU-VDN) and on the publicly available CDnet-2014 dataset. Our newly created weather-degraded video dataset, namely, TU-VDN, consists of sixty video sequences that represent four atmospheric conditions, namely, low light, dust, rain, and fog. The results of a performance comparison with 14 state-of-the-art detection techniques also demonstrate the high accuracy of the proposed technique.