时空视觉选择性注意机制的视频火焰检测

Flame Detection in Videos with Temporal-spatial Visual Selective Attention

引入计算视觉领域中的显著性思想,结合自上而下的视觉注意模型,构建了基于时空视觉选择性注意机制的视频火焰检测模型.该模型用HSV空间的V分量表示火焰的亮度显著性,用RGB空间火焰R和B分量的关系表示火焰的颜色显著性,用局部二值模式的3种形式组合成的特征向量之间的距离表示火焰的纹理显著性;为降低模型时间、空间的复杂度,采用主成分分析对局部二值模式特征向量降维,用改进的基于火焰颜色的累积差分表示火焰的运动显著性.最后经加权线性组合各静态、动态特征图,得到当前视频帧的综合显著图.对Bilkent大学火焰视频库中全部的13段火焰视频和通过互联网获得的2段非火焰视频进行实验的结果表明,与其他流行模型相比,该模型可以更准确地检测出视频序列中的火焰区域.

Followed the idea of visual saliency in computer vision and the model of visual attention with top-down,the model of flame detection in video sequences was proposed based on the temporal-spatial visual attention.The V component of the HSV color space was used to describe the saliency of flame brightness.The relation of R and B in the RGB color space was used to indicate the saliency of flame color.The distance between the feature vectors which are the combination of features with three Local Binary Patterns was applied to express the saliency of flame texture.The dimensions of LBP feature vectors were reduced before by the Principal Component Analysis to lower the computational complexity.Then the saliency of motion was generated by the improved accumulative difference based on the flame color.Finally,the integral saliency map of current frame was formed by weighted linear combination of the static saliency maps and the motion saliency map.Experiments were done on 13 fire videos in the fire video dataset from Bilkent University and 2 non-fire videos from Internet.The experimental results show that the proposed model achieves better performance on flame detection than other state-of-the-art models.