15 research outputs found
Verification of Smoke Detection in Video Sequences Based on Spatio-temporal Local Binary Patterns
AbstractThe early smoke detection in outdoor scenes using video sequences is one of the crucial tasks of modern surveillance systems. Real scenes may include objects that are similar to smoke with dynamic behavior due to low resolution cameras, blurring, or weather conditions. Therefore, verification of smoke detection is a necessary stage in such systems. Verification confirms the true smoke regions, when the regions similar to smoke are already detected in a video sequence. The contributions are two-fold. First, many types of Local Binary Patterns (LBPs) in 2D and 3D variants were investigated during experiments according to changing properties of smoke during fire gain. Second, map of brightness differences, edge map, and Laplacian map were studied in Spatio-Temporal LBP (STLBP) specification. The descriptors are based on histograms, and a classification into three classes such as dense smoke, transparent smoke, and non-smoke was implemented using Kullback-Leibler divergence. The recognition results achieved 96β99% and 86β94% of accuracy for dense smoke in dependence of various types of LPBs and shooting artifacts including noise
ΠΠΠΠΠ ΠΠ’Π ΠΠΠΠΠΠΠ ΠΠΠΠΠΠΠ§ΠΠ‘ΠΠΠ₯ Π’ΠΠΠ‘Π’Π£Π
Recognizing dynamic patterns based on visual processing is significant for many applications such as remote monitoring for the prevention of natural disasters, e.g. forest fires, various types of surveillance, e.g. traffic monitoring, background subtraction in challenging environments, e.g. outdoor scenes with vegetation, homeland security applications and scientific studies of animal behavior. In the context of surveillance, recognizing dynamic patterns is of significance to isolate activities of interest (e.g. fire) from distracting background (e.g. windblown vegetation and changes in scene illumination).Methods: pattern recognition, computer vision.Results: This paper presents video based image processing algorithm with samples usually containing a cluttered background. According to the spatiotemporal features, four categorized groups were formulated. Dynamic texture recognition algorithm refers image objects to one of this group. Motion, color, facial, energy Laws and ELBP features are extracted for dynamic texture categorization. Classification based on boosted random forest.Practical relevance: Experimental results show that the proposed method is feasible and effective for video-based dynamic texture categorization. Averaged classification accuracy on the all video images is 95.2%.ΠΠΎΡΡΠ°Π½ΠΎΠ²ΠΊΠ° ΠΏΡΠΎΠ±Π»Π΅ΠΌΡ: ΠΠ±Π½Π°ΡΡΠΆΠ΅Π½ΠΈΠ΅ Π΄ΠΈΠ½Π°ΠΌΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠ΅ΠΊΡΡΡΡ Π½Π° Π²ΠΈΠ΄Π΅ΠΎΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡΡ
Π² Π½Π°ΡΡΠΎΡΡΠ΅Π΅ Π²ΡΠ΅ΠΌΡ Π½Π°Ρ
ΠΎΠ΄ΠΈΡ Π²ΡΠ΅ Π±ΠΎΠ»Π΅Π΅ ΡΠΈΡΠΎΠΊΠΎΠ΅ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ Π² ΡΠΈΡΡΠ΅ΠΌΠ°Ρ
ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½ΠΎΠ³ΠΎ Π·ΡΠ΅Π½ΠΈΡ. ΠΠ°ΠΏΡΠΈΠΌΠ΅Ρ, ΠΎΠ±Π½Π°ΡΡΠΆΠ΅Π½ΠΈΠ΅ Π΄ΡΠΌΠ° ΠΈ ΠΏΠ»Π°ΠΌΠ΅Π½ΠΈ Π² ΡΠΈΡΡΠ΅ΠΌΠ°Ρ
ΡΠΊΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΌΠΎΠ½ΠΈΡΠΎΡΠΈΠ½Π³Π°, Π°Π½Π°Π»ΠΈΠ· Π°Π²ΡΠΎΠΌΠΎΠ±ΠΈΠ»ΡΠ½ΠΎΠ³ΠΎ ΡΡΠ°ΡΠΈΠΊΠ° ΠΏΡΠΈ ΠΌΠΎΠ½ΠΈΡΠΎΡΠΈΠ½Π³Π΅ Π·Π°Π³ΡΡΠΆΠ΅Π½Π½ΠΎΡΡΠΈ Π΄ΠΎΡΠΎΠ³, ΠΈ Π² Π΄ΡΡΠ³ΠΈΡ
ΡΠΈΡΡΠ΅ΠΌΠ°Ρ
. ΠΠΎΠΈΡΠΊ ΠΎΠ±ΡΠ΅ΠΊΡΠ° ΠΈΠ½ΡΠ΅ΡΠ΅ΡΠ° Π½Π° Π΄ΠΈΠ½Π°ΠΌΠΈΡΠ΅ΡΠΊΠΎΠΌ ΡΠΎΠ½Π΅ ΡΠ°ΡΡΠΎ Π±ΡΠ²Π°Π΅Ρ Π·Π°ΡΡΡΠ΄Π½Π΅Π½ Π·Π° ΡΡΠ΅Ρ ΠΏΠΎΡ
ΠΎΠΆΠΈΡ
ΡΠ΅ΠΊΡΡΡΡΠ½ΡΡ
ΠΏΡΠΈΠ·Π½Π°ΠΊΠΎΠ² ΠΈΠ»ΠΈ ΠΏΡΠΈΠ·Π½Π°ΠΊΠΎΠ² Π΄Π²ΠΈΠΆΠ΅Π½ΠΈΡ Ρ ΡΠΎΠ½Π° ΠΈ ΠΈΡΠΊΠΎΠΌΠΎΠ³ΠΎ ΠΎΠ±ΡΠ΅ΠΊΡΠ°. Π ΡΠ²ΡΠ·ΠΈ Ρ ΡΡΠΈΠΌ Π²ΠΎΠ·Π½ΠΈΠΊΠ°Π΅Ρ Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎΡΡΡ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠΈ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ Π΄ΠΈΠ½Π°ΠΌΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠ΅ΠΊΡΡΡΡ Π΄Π»Ρ Π²ΡΠ΄Π΅Π»Π΅Π½ΠΈΡ ΠΎΠ±ΡΠ΅ΠΊΡΠΎΠ² ΠΈΠ½ΡΠ΅ΡΠ΅ΡΠ° Π½Π° Π΄ΠΈΠ½Π°ΠΌΠΈΡΠ΅ΡΠΊΠΎΠΌ ΡΠΎΠ½Π΅.ΠΠ΅ΡΠΎΠ΄Ρ: ΡΠ°ΡΠΏΠΎΠ·Π½Π°Π²Π°Π½ΠΈΠ΅ ΠΎΠ±ΡΠ°Π·ΠΎΠ², ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½ΠΎΠ΅ Π·ΡΠ΅Π½ΠΈΠ΅.Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ: Π Π΄Π°Π½Π½ΠΎΠΉ ΡΠ°Π±ΠΎΡΠ΅ ΡΠ°ΡΡΠΌΠ°ΡΡΠΈΠ²Π°Π΅ΡΡΡ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠ° Π²ΠΈΠ΄Π΅ΠΎΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ ΡΠΎΠ΄Π΅ΡΠΆΠ°ΡΠΈΡ
ΠΎΠ±ΡΠ΅ΠΊΡΡ Ρ Π΄ΠΈΠ½Π°ΠΌΠΈΡΠ΅ΡΠΊΠΈΠΌ ΠΏΠΎΠ²Π΅Π΄Π΅Π½ΠΈΠ΅ΠΌ Π½Π° Π΄ΠΈΠ½Π°ΠΌΠΈΡΠ΅ΡΠΊΠΎΠΌ ΡΠΎΠ½Π΅, ΡΠ°ΠΊΠΈΠ΅ ΠΊΠ°ΠΊ Π²ΠΎΠ΄Π°, ΡΡΠΌΠ°Π½, ΠΏΠ»Π°ΠΌΡ, ΡΠ΅ΠΊΡΡΠΈΠ»Ρ Π½Π° Π²Π΅ΡΡΡ ΠΈ Π΄Ρ. Π Π°Π·ΡΠ°Π±ΠΎΡΠ°Π½ Π°Π»Π³ΠΎΡΠΈΡΠΌ ΠΎΡΠ½Π΅ΡΠ΅Π½ΠΈΡ ΠΎΠ±ΡΠ΅ΠΊΡΠΎΠ² Π²ΠΈΠ΄Π΅ΠΎΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡ ΠΊ ΠΎΠ΄Π½ΠΎΠΉ ΠΈΠ· ΡΠ΅ΡΡΡΠ΅Ρ
ΠΏΡΠ΅Π΄Π»Π°Π³Π°Π΅ΠΌΡΡ
ΠΊΠ°ΡΠ΅Π³ΠΎΡΠΈΠΉ. ΠΠ·Π²Π»Π΅ΠΊΠ°ΡΡΡΡ ΠΏΡΠΈΠ·Π½Π°ΠΊΠΈ Π΄Π²ΠΈΠΆΠ΅Π½ΠΈΡ, ΡΠ²Π΅ΡΠΎΠ²ΡΠ΅ ΠΎΡΠΎΠ±Π΅Π½Π½ΠΎΡΡΠΈ, ΡΡΠ°ΠΊΡΠ°Π»ΡΠ½ΠΎΡΡΠΈ, ΡΠ½Π΅ΡΠ³Π΅ΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΏΡΠΈΠ·Π½Π°ΠΊΠΈ ΠΠ°ΡΠ°, ΡΡΡΠΎΡΡΡΡ ELBP-Π³ΠΈΡΡΠΎΠ³ΡΠ°ΠΌΠΌΡ. Π ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΎΡΠ° ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ Π±ΡΡΡΠΈΠ½Π³ΠΎΠ²ΡΠΉ ΡΠ»ΡΡΠ°ΠΉΠ½ΡΠΉ Π»Π΅Ρ.ΠΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠ°Ρ Π·Π½Π°ΡΠΈΠΌΠΎΡΡΡ: Π Π°Π·ΡΠ°Π±ΠΎΡΠ°Π½ ΠΌΠ΅ΡΠΎΠ΄, ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡΡΠΈΠΉ ΡΠ°Π·Π΄Π΅Π»ΠΈΡΡ Π΄ΠΈΠ½Π°ΠΌΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΡΠ΅ΠΊΡΡΡΡ Π½Π° ΠΊΠ°ΡΠ΅Π³ΠΎΡΠΈΠΈ: ΠΏΠΎ ΡΠΈΠΏΡ Π΄Π²ΠΈΠΆΠ΅Π½ΠΈΡ (ΠΏΠ΅ΡΠΈΠΎΠ΄ΠΈΡΠ΅ΡΠΊΠΎΠ΅ ΠΈ Ρ
Π°ΠΎΡΠΈΡΠ½ΠΎΠ΅) ΠΈ ΡΠΈΠΏΡ ΠΎΠ±ΡΠ΅ΠΊΡΠΎΠ² ΠΈΠ½ΡΠ΅ΡΠ΅ΡΠ° (ΠΏΡΠΈΡΠΎΠ΄Π½ΡΠ΅ ΠΈ ΠΈΡΠΊΡΡΡΡΠ²Π΅Π½Π½ΡΠ΅). ΠΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠ°Π»ΡΠ½ΡΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΠΏΠΎΠ΄ΡΠ²Π΅ΡΠΆΠ΄Π°ΡΡ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΡ ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½Π½ΠΎΠ³ΠΎ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° Π΄Π»Ρ ΠΎΡΠ½Π΅ΡΠ΅Π½ΠΈΡ ΠΎΠ±ΡΠ΅ΠΊΡΠΎΠ² ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡ ΠΊ ΡΠΎΠΉ ΠΈΠ»ΠΈ ΠΈΠ½ΠΎΠΉ ΠΊΠ°ΡΠ΅Π³ΠΎΡΠΈΠΈ. Π‘ΡΠ΅Π΄Π½ΡΡ ΡΠΎΡΠ½ΠΎΡΡΡ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΡΠΎΡΡΠ°Π²ΠΈΠ»Π° 95.2%
Video Based Flame and Smoke Detection
Π ΡΠ°Π±ΠΎΡΠ΅ ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½ Π°Π»Π³ΠΎΡΠΈΡΠΌ ΠΎΠ±Π½Π°ΡΡΠΆΠ΅Π½ΠΈΡ ΠΏΠΎΠΆΠ°ΡΠ° ΠΏΠΎ Π²ΠΈΠ΄Π΅ΠΎΠ΄Π°Π½Π½ΡΠΌ Π½Π° ΠΎΡΠΊΡΡΡΡΡ
ΠΏΡΠΎΡΡΡΠ°Π½ΡΡΠ²Π°Ρ
, ΠΊΠΎΠ³Π΄Π° ΡΡΠ°Π΄ΠΈΡΠΈΠΎΠ½Π½ΡΠΌΠΈ ΡΠΏΠΎΡΠΎΠ±Π°ΠΌΠΈ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ Π΄Π°ΡΡΠΈΠΊΠΎΠ² Ρ
ΠΈΠΌΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΡΠΎΡΡΠ°Π²Π°
Π²ΠΎΠ·Π΄ΡΡ
Π° ΠΈΠ»ΠΈ ΡΠ΅ΠΌΠΏΠ΅ΡΠ°ΡΡΡΡ ΠΎΠ±Π½Π°ΡΡΠΆΠ΅Π½ΠΈΠ΅ Π΄ΡΠΌΠ° ΠΈ ΠΏΠ»Π°ΠΌΠ΅Π½ΠΈ Π½Π΅Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎ. ΠΠ±Π½Π°ΡΡΠΆΠ΅Π½ΠΈΠ΅ Π΄ΡΠΌΠ° ΠΈ
ΠΏΠ»Π°ΠΌΠ΅Π½ΠΈ Π²ΡΠΏΠΎΠ»Π½ΡΠ΅ΡΡΡ ΠΏΠ°ΡΠ°Π»Π»Π΅Π»ΡΠ½ΠΎ, ΠΏΠΎΠΆΠ°Ρ ΡΡΠΈΡΠ°Π΅ΡΡΡ Π½Π°ΠΉΠ΄Π΅Π½Π½ΡΠΌ Π² ΡΠ»ΡΡΠ°Π΅ Π΄Π΅ΡΠ΅ΠΊΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ
ΠΎΠ΄Π½ΠΎΠ³ΠΎ ΠΎΠ±ΡΠ΅ΠΊΡΠ°: Π΄ΡΠΌΠ° ΠΈΠ»ΠΈ ΠΏΠ»Π°ΠΌΠ΅Π½ΠΈ. ΠΠ»Π³ΠΎΡΠΈΡΠΌ Π½Π°Ρ
ΠΎΠΆΠ΄Π΅Π½ΠΈΡ Π΄ΡΠΌΠ° ΠΈ ΠΏΠ»Π°ΠΌΠ΅Π½ΠΈ ΠΎΡΠ½ΠΎΠ²Π°Π½ Π½Π°
Π°Π½Π°Π»ΠΈΠ·Π΅ ΠΏΡΠΎΡΡΡΠ°Π½ΡΡΠ²Π΅Π½Π½ΠΎ-Π²ΡΠ΅ΠΌΠ΅Π½Π½ΡΡ
ΠΏΡΠΈΠ·Π½Π°ΠΊΠΎΠ². ΠΠ° ΠΏΠ΅ΡΠ²ΠΎΠΌ ΡΡΠ°ΠΏΠ΅ ΠΎΠ±Π½Π°ΡΡΠΆΠ΅Π½ΠΈΡ Π΄ΡΠΌΠ°
Π²ΡΠΏΠΎΠ»Π½ΡΠ΅ΡΡΡ ΠΏΠΎΠΈΡΠΊ Π΄Π²ΠΈΠΆΠ΅Π½ΠΈΡ Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° ΡΠΎΠΏΠΎΡΡΠ°Π²Π»Π΅Π½ΠΈΡ Π±Π»ΠΎΠΊΠΎΠ², Π·Π°ΡΠ΅ΠΌ
ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΠΈΡΡΡ Ρ
ΡΠΎΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΠΉ Π°Π½Π°Π»ΠΈΠ· Π΄Π²ΠΈΠΆΡΡΠΈΡ
ΡΡ ΠΎΠ±Π»Π°ΡΡΠ΅ΠΉ, ΡΡΠ΅Ρ ΡΡΡΠ±ΡΠ»Π΅Π½ΡΠ½ΠΎΡΡΠΈ.
ΠΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΡ ΠΎΠ±Π»Π°ΡΡΠ΅ΠΉ-ΠΊΠ°Π½Π΄ΠΈΠ΄Π°ΡΠΎΠ² ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΠΈΡΡΡ Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΠΌΠ°ΡΠΈΠ½Ρ ΠΎΠΏΠΎΡΠ½ΡΡ
Π²Π΅ΠΊΡΠΎΡΠΎΠ². ΠΠ΅ΡΠΈΡΠΈΠΊΠ°ΡΠΈΡ Π²ΡΠΏΠΎΠ»Π½Π΅Π½Π° Π½Π° Π±Π°Π·Π΅ ΠΏΡΠΎΡΡΡΠ°Π½ΡΡΠ²Π΅Π½Π½ΠΎ-Π²ΡΠ΅ΠΌΠ΅Π½Π½ΡΡ
Π»ΠΎΠΊΠ°Π»ΡΠ½ΡΡ
Π±ΠΈΠ½Π°ΡΠ½ΡΡ
ΡΠ°Π±Π»ΠΎΠ½ΠΎΠ². ΠΠ»Ρ ΠΎΠ±Π½Π°ΡΡΠΆΠ΅Π½ΠΈΡ ΠΏΠ»Π°ΠΌΠ΅Π½ΠΈ Π²Π·ΡΡΠ° ΡΡΠ½ΠΊΡΠΈΡ Background Subtraction Π±ΠΈΠ±Π»ΠΈΠΎΡΠ΅ΠΊΠΈ
ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½ΠΎΠ³ΠΎ Π·ΡΠ΅Π½ΠΈΡ OpenCV, Π²ΡΠΏΠΎΠ»Π½Π΅Π½ ΡΡΠ΅Ρ ΡΠ²Π΅ΡΠΎΠ²ΡΡ
ΠΎΡΠΎΠ±Π΅Π½Π½ΠΎΡΡΠ΅ΠΉ ΠΏΠ»Π°ΠΌΠ΅Π½ΠΈ ΠΈ Π°Π½Π°Π»ΠΈΠ· Π΅Π³ΠΎ
Π΄ΠΈΠ½Π°ΠΌΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠ²ΠΎΠΉΡΡΠ². ΠΠ»Ρ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΈΡ ΡΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠ°Π»ΡΠ½ΡΡ
ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½Ρ Π±Π°Π·Ρ
Π΄Π°Π½Π½ΡΡ
Π²ΠΈΠ΄Π΅ΠΎΠΏΠΎΡΠ»Π΅Π΄ΠΎΠ²Π°ΡΠ΅Π»ΡΠ½ΠΎΡΡΠ΅ΠΉ ΠΠΈΠ»ΡΠΊΠ΅Π½ΡΡΠΊΠΎΠ³ΠΎ ΡΠ½ΠΈΠ²Π΅ΡΡΠΈΡΠ΅ΡΠ° ΠΈ Dyntex. ΠΠΎΠΏΠΎΠ»Π½ΠΈΡΠ΅Π»ΡΠ½ΠΎ
ΡΠ΅ΠΏΡΠ΅Π·Π΅Π½ΡΠ°ΡΠΈΠ²Π½ΠΎΡΡΡ ΡΠ΅ΡΡΠΎΠ²ΠΎΠ³ΠΎ Π½Π°Π±ΠΎΡΠ° Π²ΠΈΠ΄Π΅ΠΎΡΠΎΠ»ΠΈΠΊΠΎΠ² ΠΏΠΎΠ²ΡΡΠ΅Π½Π° Π΄Π°Π½Π½ΡΠΌΠΈ Ρ ΡΠ΅Π°Π»ΡΠ½ΡΡ
ΠΊΠ°ΠΌΠ΅Ρ Π²ΠΈΠ΄Π΅ΠΎΠ½Π°Π±Π»ΡΠ΄Π΅Π½ΠΈΡ, Π² ΡΠΎΠΌ ΡΠΈΡΠ»Π΅ ΠΏΠΎΠ»ΡΡΠ΅Π½Π½ΡΠΌΠΈ Π² Π½ΠΎΡΠ½ΠΎΠ΅ Π²ΡΠ΅ΠΌΡ. ΠΠΎΠ»ΠΈΡΠ΅ΡΡΠ²ΠΎ ΠΊΠ°Π΄ΡΠΎΠ²
ΡΠ΅ΡΡΠΎΠ²ΡΡ
Π²ΠΈΠ΄Π΅ΠΎΠΏΠΎΡΠ»Π΅Π΄ΠΎΠ²Π°ΡΠ΅Π»ΡΠ½ΠΎΡΡΠ΅ΠΉ ΡΠΎΡΡΠ°Π²ΠΈΠ»ΠΎ 44 406, ΠΎΠ±ΡΠ°Ρ ΠΏΡΠΎΠ΄ΠΎΠ»ΠΆΠΈΡΠ΅Π»ΡΠ½ΠΎΡΡΡ
ΡΠΎΠ»ΠΈΠΊΠΎΠ² β 40 ΠΌΠΈΠ½. Π‘ΡΠ΅Π΄Π½ΡΡ ΡΠΎΡΠ½ΠΎΡΡΡ ΠΎΠ±Π½Π°ΡΡΠΆΠ΅Π½ΠΈΡ Π΄ΡΠΌΠ° ΡΠΎΡΡΠ°Π²ΠΈΠ»Π° 98 %, ΠΏΠ»Π°ΠΌΠ΅Π½ΠΈ β 94,9 %.
ΠΠΎΠΆΠ½ΠΎΠΏΠΎΠ»ΠΎΠΆΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΡΠ°Π±Π°ΡΡΠ²Π°Π½ΠΈΡ ΠΏΡΠΈ ΠΎΠ±Π½Π°ΡΡΠΆΠ΅Π½ΠΈΠΈ Π΄ΡΠΌΠ° ΠΈ ΠΏΠ»Π°ΠΌΠ΅Π½ΠΈ Π² ΡΡΠ΅Π΄Π½Π΅ΠΌ ΡΠ°Π²Π½Ρ
3,46 %. Π’Π°ΠΊΠΈΠΌ ΠΎΠ±ΡΠ°Π·ΠΎΠΌ, ΡΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠ°Π»ΡΠ½ΡΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΠΏΠΎΠ΄ΡΠ²Π΅ΡΠΆΠ΄Π°ΡΡ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΡ
ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½Π½ΠΎΠ³ΠΎ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° ΠΎΠ±Π½Π°ΡΡΠΆΠ΅Π½ΠΈΡ ΠΏΠ»Π°ΠΌΠ΅Π½ΠΈ ΠΈ Π΄ΡΠΌΠ° ΠΏΠΎ Π²ΠΈΠ΄Π΅ΠΎΠΏΠΎΡΠ»Π΅Π΄ΠΎΠ²Π°ΡΠ΅Π»ΡΠ½ΠΎΡΡΡΠΌ Π½Π°
ΠΎΡΠΊΡΡΡΡΡ
ΠΏΡΠΎΡΡΡΠ°Π½ΡΡΠ²Π°Ρ
The early fire detection in outdoor scenes using video sequences is one of crucial tasks of modern
surveillance systems in urban and terrestrial natural environment. A conventional way of video
analysis is to design a good background model and to track a motion selectively. Color, contours,
fractal properties, and transparency, are considered the main spatial properties of smoke and flame
in a still image or a single frame. Smoke detections algorithm steps. First, local smoke regions are
detected based on motion estimation and chromatic analysis. The clustering of such local regions
provides global smoke regions in a scene. At this stage, smoke and non-smoke regions are analyzed
in order to exclude errors of false rejection. The suspicious region is extracted by using blockmatching
algorithm. Second, global regions are verified by using statistical and temporal features.
In this research, smoke colored blocks and turbulence characteristics. Verification based on spatiotemporal
local binary patterns. An automatic flame detection method uses the features of fire, such
as the moving parameters, chromatic components, and geometrical (flickering) features. A candidate
fire region is determined according to the color component ratio and motion cue of fire flame obtained
by background subtraction. The flame color probability is then estimated based threshold value in
the combination of RGB and YSV color spaces. The motion probability obtained is by employing the
background model with Background Subtractor function in OpenCV (Open Source Computer Vision
Library). Flames flicker in height, size and in brightness. Video based flame detection algorithms often
analyze flickering of pixel intensities over time to detect flames. In this study we investigate five different
pixel intensity flickering features based on methods presented in previous work. For flickering features
we calculate geometry, compare frequency of initial frame with fire re-gion candidate, and check
the change in the size of the rectangular flame candidate block.Flame and smoke regions classifier
using support vector machine. Video based flame and smoke detection is carried out in parallel.For
experimental researches the database of dynamic textures Dyntex and database of Bilkent University
were used. The developed method of smoke detection on video provides 94.9β98% of accuracy for fire
detection. Experimental results show that the proposed method is feasible and effective for video based
flame and smoke detectio
Trends in the global market for the transfer of intellectual property
The article is aimed at conducting a comprehensive study of opportunities and prospects of development of methodology and practice of organization of innovation processes in the economies of various countries, development of recommendations to increase the efficiency of innovation activity.The relevance of this topic is due to the need to create effective mechanisms of expert and analytical support of a high level of innovation development, the importance of providing measures to support technologies - Β«catalystsΒ» socio-economic development of economic agents, the key role of technology transfer as a condition for ensuring and maintaining innovative activity of economic agents. Keywords: innovation, innovative technologies, intellectual property transfer
BUILDING THE PROFILE OF THE SUBSCRIBER OF MOBILE NETWORKS BASED ON ONTOLOGICAL APPROACH
Π¦Π΅Π»Ρ.Β Π ΡΠ²ΡΠ·ΠΈ Ρ ΡΠ²Π΅Π»ΠΈΡΠ΅Π½ΠΈΠ΅ΠΌ ΡΠΈΡΠ»Π° Π°Π±ΠΎΠ½Π΅Π½ΡΠΎΠ² ΠΌΠΎΠ±ΠΈΠ»ΡΠ½ΡΡ
ΡΠ΅ΡΠ΅ΠΉ, ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΠ΅ΠΌΡΡ
Π°Π±ΠΎΠ½Π΅Π½ΡΠ°ΠΌΠΈ ΡΡΡΡΠΎΠΉΡΡΠ², Π° ΡΠ°ΠΊΠΆΠ΅ Π²ΡΡΠΎΠΊΠΎΠΉ Π°ΠΊΡΠΈΠ²Π½ΠΎΡΡΡΡ Π°Π±ΠΎΠ½Π΅Π½ΡΠΎΠ² Π°Π³ΡΠ΅Π³ΠΈΡΡΠ΅ΠΌΠ°Ρ ΠΎΠ± Π°ΡΡΠΈΠ±ΡΡΠ°Ρ
Π°Π±ΠΎΠ½Π΅Π½ΡΠΎΠ² ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΡ Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠ° Π΄Π»Ρ Π²ΡΡΡΡΠ°ΠΈΠ²Π°Π½ΠΈΡ ΡΠ΅ΠΊΠΎΠΌΠ΅Π½Π΄Π°ΡΠ΅Π»ΡΠ½ΡΡ
ΡΡΠ½ΠΊΡΠΈΠΉ ΡΠΈΡΡΠ΅ΠΌ ΡΠ΅Π»Π΅ΠΊΠΎΠΌΠΌΡΠ½ΠΈΠΊΠ°ΡΠΈΠΎΠ½Π½ΡΡ
ΠΊΠΎΠΌΠΏΠ°Π½ΠΈΠΉ, ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΈΡ ΠΌΠ°ΡΠΊΠ΅ΡΠΈΠ½Π³ΠΎΠ²ΡΡ
ΠΈΠ½ΠΈΡΠΈΠ°ΡΠΈΠ², ΠΏΠΎΠ²ΡΡΠ΅Π½ΠΈΡ ΠΊΠ°ΡΠ΅ΡΡΠ²Π° ΠΎΠΊΠ°Π·ΡΠ²Π°Π΅ΠΌΡΡ
ΡΡΠ»ΡΠ³, ΠΏΡΠΈ ΠΏΡΠΎΠ³Π½ΠΎΠ·ΠΈΡΠΎΠ²Π°Π½ΠΈΠΈ ΠΏΠΎΡΡΠ΅Π±Π½ΠΎΡΡΠ΅ΠΉ ΠΈ ΠΆΠ΅Π»Π°Π½ΠΈΠΉ ΠΊΠ»ΠΈΠ΅Π½ΡΠΎΠ², Π° ΡΠ°ΠΊΠΆΠ΅ Π΄Π»Ρ ΠΌΠ½ΠΎΠ³ΠΈΡ
Π΄ΡΡΠ³ΠΈΡ
ΡΡΠ½ΠΊΡΠΈΠΉ. Π‘ΡΠ°ΡΡΡ ΠΏΠΎΡΠ²ΡΡΠ΅Π½Π° ΡΠ°ΡΡΠΌΠΎΡΡΠ΅Π½ΠΈΠ΅ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ², Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½Π½ΡΡ
Π½Π° ΡΠΎΡΠΌΠ°Π»ΠΈΠ·Π°ΡΠΈΡ ΠΏΡΠ΅Π΄ΠΌΠ΅ΡΠ½ΠΎΠΉ ΠΎΠ±Π»Π°ΡΡΠΈ ΠΏΡΠΈ ΠΏΠΎΡΡΡΠΎΠ΅Π½ΠΈΠΈ ΠΏΡΠΎΡΠΈΠ»Π΅ΠΉ Π°Π±ΠΎΠ½Π΅Π½ΡΠΎΠ² ΠΌΠΎΠ±ΠΈΠ»ΡΠ½ΠΎΠΉ ΡΠ²ΡΠ·ΠΈ.ΠΠ΅ΡΠΎΠ΄Ρ.Β Π ΡΠ°Π±ΠΎΡΠ΅ ΡΠ°ΡΡΠΌΠ°ΡΡΠΈΠ²Π°ΡΡΡΡ ΠΌΠ΅ΡΠΎΠ΄ ΡΠΎΡΠΌΠ°Π»ΡΠ½ΡΡ
ΠΏΠΎΠ½ΡΡΠΈΠΉ, Π° ΡΠ°ΠΊΠΆΠ΅ ΠΌΠΎΠ΄Π΅Π»Ρ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½ΠΈΡ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ Π½Π° ΠΊΠΎΠ½ΡΠ΅ΠΏΡΡΠ°Π»ΡΠ½ΠΎΠΌΒ ΡΡΠΎΠ²Π½Π΅ Π² ΠΊΠΎΠ½ΡΠ΅ΠΊΡΡΠ΅ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½ΠΈΡ Π·Π½Π°Π½ΠΈΠΉ ΠΎΠ± Π°Π±ΠΎΠ½Π΅Π½ΡΠ°Ρ
ΠΌΠΎΠ±ΠΈΠ»ΡΠ½ΠΎΠΉ ΡΠ²ΡΠ·ΠΈ.Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ.Β ΠΠ° ΠΎΡΠ½ΠΎΠ²Π΅ ΠΈΠ·ΡΡΠ΅Π½ΠΈΡ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² ΡΡΡΡΠΊΡΡΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π·Π½Π°Π½ΠΈΠΉ ΠΎ ΠΏΡΠ΅Π΄ΠΌΠ΅ΡΠ½ΠΎΠΉ ΠΎΠ±Π»Π°ΡΡΠΈ Π°Π²ΡΠΎΡΠ°ΠΌΠΈ ΠΏΡΠ΅Π΄Π»Π°Π³Π°Π΅ΡΡΡ ΠΌΠΎΠ΄Π΅Π»Ρ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½ΠΈΡ ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎΠΉ ΠΈ ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎΠΉ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ ΠΎΠ± ΠΎΠ±ΡΠ΅ΠΊΡΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΠΎΠ½ΡΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄Π°.Purpose. In connection with the increase in the number of mobile network subscribers used by device users, as well as the high activity of subscribers, information aggregated about the attributes of subscribers is necessary for building advisory functions of telecommunications companiesβ systems, conducting marketing initiatives, improving the quality of services provided, predicting the needs and desires of customers, and for many other functions. The article is devoted to the consideration of methods aimed at formalization of the subject domain in the construction of profiles of mobile communication subscribers.Methods. The paper considers the method of formal concepts, as well as the model of information representation at the conceptual level in the context of knowledge representation about mobile communication subscribers.Results. On the basis of studying the methods of structuring knowledge of the subject domain, the authors propose a model for presenting qualitative and quantitative information about the object of research using the ontological approach
Dynamic texture recognition under adverse lighting and weather conditions for outdoor environments
Recognizing dynamic patterns based on visual processing is significant for many applications. In this paper dynamic texture recognition focuses on outdoor scenarios where a crisis event might occur (i.e. fire in a forest, floods/flooding etc.) Real outdoor scenes may include the objects with dynamic behaviour due to illumination, blurring, or weather conditions effects. Under bad weather conditions the imaging systems is degraded and produce low visibility images. In this work precipitation artefacts and lightning effects for dynamic texture analysis were studied. Experimental results show that the proposed method of weather and adverse lighting effects compensation is feasible and effective for videobased dynamic texture analysis under bad weather conditions
Artificial neural network technology for lips reading
The paper presents the use of neural networks for the task of automated speech reading by lips articulation. Speech recognition is performed in two stages. First, a face search is performed and the lips area is selected in a separate frame of the video sequence using Haar features. Then the sequence of frames goes to the input of deep learning convolutional and recurrent neural networks for speech viseme recognition. Experimental studies were carried out using independently obtained videos with Russian-speaking speakers
Verification of Smoke Detection in Video Sequences Based on Spatio-temporal Local Binary Patterns
Π’Π΅ΠΊΡΡ ΡΡΠ°ΡΡΠΈ Π½Π΅ ΠΏΡΠ±Π»ΠΈΠΊΡΠ΅ΡΡΡ Π² ΠΎΡΠΊΡΡΡΠΎΠΌ Π΄ΠΎΡΡΡΠΏΠ΅ Π² ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΠΈΠΈ Ρ ΠΏΠΎΠ»ΠΈΡΠΈΠΊΠΎΠΉ ΠΆΡΡΠ½Π°Π»Π°.The early smoke detection in outdoor scenes using video sequences is one of the crucial tasks of modern surveillance systems. Real scenes may include objects that are similar to smoke with dynamic behavior due to low resolution cameras, blurring, or weather conditions. Therefore, verification of smoke detection is a necessary stage in such systems. Verification confirms the true smoke regions, when the regions similar to smoke are already detected in a video sequence. The contributions are two-fold. First, many types of Local Binary Patterns (LBPs) in 2D and 3D variants were investigated during experiments according to changing properties of smoke during fire gain. Second, map of brightness differences, edge map, and Laplacian map were studied in Spatio-Temporal LBP (STLBP) specification. The descriptors are based on histograms, and a classification into three classes such as dense smoke, transparent smoke, and non-smoke was implemented using Kullback-Leibler divergence. The recognition results achieved 96β99% and 86β94% of accuracy for dense smoke in dependence of various types of LPBs and shooting artifacts including noise
Texture analysis in watermarking paradigms
Π’Π΅ΠΊΡΡ ΡΡΠ°ΡΡΠΈ Π½Π΅ ΠΏΡΠ±Π»ΠΈΠΊΡΠ΅ΡΡΡ Π² ΠΎΡΠΊΡΡΡΠΎΠΌ Π΄ΠΎΡΡΡΠΏΠ΅ Π² ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΠΈΠΈ Ρ ΠΏΠΎΠ»ΠΈΡΠΈΠΊΠΎΠΉ ΠΆΡΡΠ½Π°Π»Π°.Digital watermarking algorithms have been developed rapidly as a response on the challenges caused by various internet attacks that are distorted the content of the host image and watermark partially or fully. In this paper, the issues of texture analysis with a goal to detect the most suitable image areas for embedding are discussed. The statistical and model-based methods are investigated as a trade-off between the computational cost and quality of the detected areas, where the embedded bits of a watermark could be the most invisible for a human vision. The criteria for detection of such areas based on the textural, contrast, illumination, and color coherence of the host image and watermark are formulated. The experiments show that the statistical methods based on the gradient oriented Local Binary Patterns (LBP) provide better computational time regarding to fractal estimation of textural image areas
Spatio-temporal Smoke Clustering in Outdoor Scenes Based on Boosted Random Forests
Π’Π΅ΠΊΡΡ ΡΡΠ°ΡΡΠΈ Π½Π΅ ΠΏΡΠ±Π»ΠΈΠΊΡΠ΅ΡΡΡ Π² ΠΎΡΠΊΡΡΡΠΎΠΌ Π΄ΠΎΡΡΡΠΏΠ΅ Π² ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΠΈΠΈ Ρ ΠΏΠΎΠ»ΠΈΡΠΈΠΊΠΎΠΉ ΠΆΡΡΠ½Π°Π»Π°.Nowadays, vision-based techniques for automatic early smoke detection in the outdoor scenes are in a hot topic of computer vision. The basic set of features includes the traditional features describing the spatial ones, such as color, shape, transparency, energy, and fractal property, and the temporal ones, such as frame difference estimator, motion estimator, and flicker on boundaries. The main problem of the early smoke detection is to obtain the low values of the clustering errors. Our contribution deals with a reasonable clustering of the smoke/non-smoke regions based on the Boosted Random Forests (BRFs). The BRFs provide better clustering results in comparison with the traditional clustering techniques, as well as the ordinary random forests. Forty test video sequences with and without smoke were analyzed during experiments. The true recognition results of a smoke detection achieved 97.8% that is better on 3β4% of the results obtaining by the Support Vector Machine (SVM) application. False reject rate and false acceptance rate values were significantly decreased till 3.68% and 3.24% in average, respectively