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摘要:
针对外部环境的多变性和复杂性导致的单一波段下行人检测准确率较低的问题，提出了一种改进的基于可见和红外双波段聚合通道特征的行人检测算法。分别提取可见图像与红外图像的聚合通道特征；通过改变像素对比规则，采用自适应的阈值进行比较，将得到的改进的中心对称的局部二值模式特征添加到特征通道中；针对多光谱聚合通道特征设计了不同滤波器组进行滤波；训练分类器，实现多光谱下行人检测。实验表明，改进的局部二值模式特征能更好地描述红外图像中行人的对称性，中间滤波层丰富了候选特征池，算法在多种场景均能有效检测出行人，提高了行人检测精度，与利用多光谱聚合积分通道的检测工作相比，平均漏检率有所降低。
Abstract:To solve the problem of low pedestrian detection accuracy in a single band due to variability and complexity of external environment, an improved pedestrian detection algorithm based on multispectral aggregate channel feature is proposed. The aggregate channel features of visible images and infrared images are extracted, respectively. The pixel contrast rule is changed and the results are compared with the adaptive threshold. The improved central symmetric local binary pattern feature is added to the feature channels. Different filter banks are designed to filter the multispectral aggregate channel features. The classifier is trained to realize the multispectral pedestrian detection. Experiments show that the improved local binary pattern feature can describe the symmetry of pedestrians of infrared images better and the intermediate filter layer enriches the candidate feature pool. The algorithm can effectively detect pedestrians in various scenes and improve the pedestrian detection accuracy. Compared with the previous multispectral aggregate channel detection work, the algorithm reduces the log-average miss rate.
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Key words:
- pedestrian detection /
- multispectral /
- local binary pattern /
- aggregate channel /
- filter banks
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Pedestrian detection is the principal technique for various applications, such as surveillance, tracking system and autonomous driving. Although the topic has been intensively investigated and significant improvement has been achieved in recent years, pedestrian detection is still a challenging task, limited by occluded appearances, cluttered backgrounds, and low image resolution. Besides, since most of recent researches focus on the detection of pedestrians in visible spectrum images, they are very likely to be stuck with images captured at night or bad lighting. However, ambient lighting has less effect on thermal imaging. Thermal images usually present clear silhouettes of human, but lose fine visual details of pedestrian, which can be captured by visual cameras. To overcome the drawbacks of visible images, it's helpful to fuse the information of visible images and long wave length infrared images. Aggregate channel feature is an easy but useful way to detect pedestrian. However, it only uses the information of visible spectrum images. For the above reasons, an improved pedestrian detection algorithm based on multispectral aggregate channel features is proposed. First, the aggregate channel features of the visible image and the infrared image are extracted, respectively. Specifically, the channel features extracted from the visible images include three LUV color channel features, one normalized gradient magnitude channel feature, and six histogram of oriented gradients channel (HOG) features. The channel features extracted from infrared images include one brightness channel feature and nine HOG features. All the channel features make up the aggregate multispectral channel features. Then, to use the symmetry information of pedestrian in infrared images, the improved central symmetric local binary pattern is proposed. The improved pattern feature is achieved by changing the pixel contrast rule and comparing the contrast result with the adaptive threshold. The improved central symmetric local binary pattern feature is added to feature channels to get the aggregate multispectral channel features. Next, to learn more local features and observe the effect of filters, different filter banks are designed to filter the aggregate multispectral channel features. Finally, the real adaptive boosting learning method is used to train the classifier to realize the multispectral pedestrian detection. Experiments show that the improved local binary pattern feature can better describe the symmetry of pedestrians of infrared images and the intermediate filter layer enriches the candidate feature pool. The algorithm makes use of the complementary information provided by color and thermal images, which can effectively detect pedestrians in various scenes and improve pedestrian detection accuracy. Compared with the previous multispectral aggregate channel detection work, the algorithm reduces the log-average miss rate.
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图 1 改进的多光谱聚合通道特征的行人检测算法流程.
Figure 1. Flow chart of improved multispectral aggregate channel feature for pedestrian detection.
图 2 8邻域下的LBP和CS-LBP的编码方式.
Figure 2. LBP and CS-LBP features for a neighborhood of eight pixels.
图 3 不同滤波器组形式. (a) LDCF滤波器组. (b)随机滤波器组. (c)棋盘格滤波器组.
Figure 3. Illustration of different filter banks. (a) LDCF filter bank. (b) Random filter bank. (c) Checkerboard filter bank.
图 5 不同场景的检测结果. (a)仅利用可见波段训练并在可见图像检测的结果. (b)利用MMACF算法在可见图像检测的结果. (c)利用MMACF算法在红外图像检测的结果.
Figure 5. Detection results of different scenes. (a) Detection results of a visual images trained by visual band. (b) Detection results of visual images trained by MMACF. (c) Detection results of infrared images trained by MMACF.
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