This paper proposed an adaptive multi-object pedestrian recognition algorithm based on SLIC. First, we used SLIC to superpixel the pre-segmentation processing on the image. Then, the hash distance is added as the superpixel point aggregation parameter based on the traditional superpixel measurement unit of LAB color space distance and position distance. Finally, we identified the clustering subject by using the extreme learning machine neural network. The proposed method can adaptively determine the number of superpixels to achieve high recognition performance. This method simply needs to preset the number of pre-segments, which can reduce the number of detection targets, improve the segmentation efficiency, and shorten the image identification time.

提出了一种基于SLIC的自适应多目标行人识别算法。首先，我们使用SLIC对图像进行预像素分割处理。然后，基于传统的LAB色彩空间距离和位置距离的超像素测量单位，将哈希距离添加为超像素点聚合参数。最后，我们使用极限学习机神经网络确定了聚类主题。所提出的方法可以自适应地确定超像素的数量以实现高识别性能。该方法只需要预设预分割数，可以减少检测目标的数量，提高分割效率，缩短图像识别时间。