An obstacle recognition method applied to an automatic traveling device, comprising: obtaining an image of an environment in a traveling direction of an automatic traveling device; separating out a chrominance channel image and a luminance channel image according to the image; performing filtration and normalization pre-processing steps and edge processing on the luminance channel image to obtain an edge image; performing histogram statistics on the chrominance channel image to obtain the number of pixels with a maximum color proportion within a preset chrominance interval range of 15-180, denoted as maxH; performing segmentation and contour processing on the chrominance channel image to obtain a chrominance segmentation threshold and a contour image; performing contour detection on the contour image to obtain a contour block; collecting statistics on feature values corresponding to the contour block in the edge image and the contour image by: obtaining position information of the contour block; comparing the position information of the contour block with a preset position threshold to obtain a target contour block; and collecting statistics on the feature values corresponding to the target contour block in the edge image and the contour image, wherein the feature values comprise a contour size feature value, an average roughness value, a proportion of black pixel points contained in the contour block, and optionally a proportion of pixels within a chrominance segmentation threshold range; and comparing the maxH and the feature values with a plurality of different preset obstacle recognition and determination conditions, wherein if the maxH and the feature values satisfy one or more of the plurality of different preset obstacle recognition and determination conditions, the method further comprises recognizing that the image has an obstacle region; and if the maxH and the feature values do not satisfy any of the plurality of different preset obstacle recognition and determination conditions, the method further comprises recognizing that the image is an image to be filtered. obtaining an image of an environment in a traveling direction of an automatic traveling device; separating out a chrominance channel image and a luminance channel image according to the image; performing filtration and normalization pre-processing steps and edge processing on the luminance channel image to obtain an edge image; performing histogram statistics on the chrominance channel image to obtain the number of pixels with a maximum color proportion within a preset chrominance interval range of 15-180, denoted as maxH; performing segmentation and contour processing on the chrominance channel image to obtain a chrominance segmentation threshold and a contour image; performing contour detection on the contour image to obtain a contour block; collecting statistics on feature values corresponding to the contour block in the edge image and the contour image by: obtaining position information of the contour block; comparing the position information of the contour block with a preset position threshold to obtain a target contour block; and collecting statistics on the feature values corresponding to the target contour block in the edge image and the contour image, wherein the feature values comprise a contour size feature value, an average roughness value, a proportion of black pixel points contained in the contour block, and optionally a proportion of pixels within a chrominance segmentation threshold range; and obtaining position information of the contour block; comparing the position information of the contour block with a preset position threshold to obtain a target contour block; and collecting statistics on the feature values corresponding to the target contour block in the edge image and the contour image, wherein the feature values comprise a contour size feature value, an average roughness value, a proportion of black pixel points contained in the contour block, and optionally a proportion of pixels within a chrominance segmentation threshold range; and comparing the maxH and the feature values with a plurality of different preset obstacle recognition and determination conditions, wherein if the maxH and the feature values satisfy one or more of the plurality of different preset obstacle recognition and determination conditions, the method further comprises recognizing that the image has an obstacle region; and if the maxH and the feature values do not satisfy any of the plurality of different preset obstacle recognition and determination conditions, the method further comprises recognizing that the image is an image to be filtered.