He Miao, Luo Haibo, Chang Zheng, Hui Bin
Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China.
Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang 110016, China.
Sensors (Basel). 2017 Nov 22;17(11):2699. doi: 10.3390/s17112699.
For many pedestrian detectors, background vs. foreground errors heavily influence the detection quality. Our main contribution is to design semantic regions of interest that extract the foreground target roughly to reduce the background vs. foreground errors of detectors. First, we generate a pedestrian heat map from the input image with a full convolutional neural network trained on the Caltech Pedestrian Dataset. Next, semantic regions of interest are extracted from the heat map by morphological image processing. Finally, the semantic regions of interest divide the whole image into foreground and background to assist the decision-making of detectors. We test our approach on the Caltech Pedestrian Detection Benchmark. With the help of our semantic regions of interest, the effects of the detectors have varying degrees of improvement. The best one exceeds the state-of-the-art.
对于许多行人检测器而言，背景与前景误差严重影响检测质量。我们的主要贡献在于设计感兴趣语义区域，其大致提取前景目标以减少检测器的背景与前景误差。首先，我们使用在加州理工学院行人数据集上训练的全卷积神经网络从输入图像生成行人热图。接下来，通过形态图像处理从热图中提取感兴趣语义区域。最后，感兴趣语义区域将整个图像划分为前景和背景，以辅助检测器进行决策。我们在加州理工学院行人检测基准上测试我们的方法。借助我们的感兴趣语义区域，检测器的效果有不同程度的提升。最佳效果超过了当前最先进的水平。
