Towards Real-Time Obstacle Detection Using Stereo Images

This work aims at defining a new approach for real-time obstacle detection based on sparse disparity map. To achieve fast time execution, the image processing is performed on a subset of points. The proposed method begins by extracting the primitives of pixels of the two images. Then, a detection of the ground is made to limit obstacles area using color information. Subsequently, an extraction of subset of points is performed using the features extracted in the previous step. The use of the extracted features as well as the subset of points will enable us to calculate a sparse disparity map which is used to calculate the v-disparity image. The use of v-disparity image allows us to extract the obstacle profile in order to eliminate disparities of the ground. To eliminate the obstacles belonging to the background, we use the ground limits to remove all object points outside these limits. Finally, a segmentation based on disparity value of the remaining points is applied to extract obstacles. To end, validation step is used to remove false positives. The experimental results obtained are interesting with real time execution. This allows early detection to prevent collisions.