Video-based Motion Capture Algorithms and Simulation Experiments

Video-based motion capture technology is widely applied in multiple fields. This study proposes an innovative algorithm system integrating OpenCV, GAN and Snake models, dedicated to improving the accuracy and efficiency of motion capture. In terms of algorithm design, with the powerful image processing capabilities of OpenCV, noise reduction, enhancement and standardization processing are carried out on the video, effectively improving the quality of the original video. GAN is introduced and the adversarial mechanism between the generator and the discriminator is utilized to accurately extract the target motion features from the complex background. The energy function is optimized based on the Snake model to achieve dynamic and precise fitting of the target motion trajectory. We need to build a simulation experiment platform and select video datasets covering multiple scenarios for testing. Studies show that this algorithm system significantly enhances the accuracy of motion capture in complex environments, but there is still room for optimization in terms of real-time processing speed and cross-scene generalization ability.