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In order to improve the accuracy of pedestrian contour parameter extraction and realize the real-time and stable tracking of the target to be monitored, a pedestrian contour extraction and target detection algorithm based on Cauchy model is proposed. Based on the Cauchy distribution principle, the maximum likelihood value of pedestrian contour target is estimated, and then combined with the calculation of the second type of statistics method, the pedestrian target statistical modeling based on Cauchy model is completed. On this basis, a convolution neural network is established, and Gabor pedestrian contour features are extracted by convolution and deconvolution parameters. Under the function of target image segmentation theory, all pedestrian targets in the given area are identified, all kinds of existing pedestrian targets are continuously marked, and pedestrian contour targets are detected in real time, and pedestrian contour extraction and target detection based on Cauchy model are realized. The experimental results show that compared with Kinect detection algorithm, the detection accuracy of pedestrian contour is improved to 93% after using Cauchy algorithm, while the measured PTR index is reduced to 3.97, which can effectively realize the real-time and stable tracking of pedestrian contour target to be monitored.