Design and implementation of multi-feature fusion moving target detection algorithms in a complex environment based on SiamMask

Moving target recognition in a complex environment is recently an important research direction in the field of image recognition. The current research focus is how to track moving objects online in complex scenes to meet the real-time and reliability requirements of image tracking and subsequent processing. With the in-depth application of unmanned factory, intelligent safety supervision and other technologies in the field of manufacturing industry, dynamic recognition technology in the complex environment represented by a visual recognition warning system has become an important research in the field of intelligent industry, and the detection requirements of high reliability and real-time for mobile target detection have been identified. In the industrial level vision recognition warning system described in this paper, the hair area of operators was difficult to be segmented in real time because of its irregular movement. To solve this problem, a space-time predictive moving target tracking algorithm was proposed based on the SiamMask model. This algorithm combined the SiamMask single target tracking algorithm based on the PyTorch deep learning framework with ROI detection and STC spatiotemporal context prediction algorithm. According to the online learning of the spatiotemporal relationship of the target, it predicted the new target location and corrected the algorithm of the SiamMask model to realize the fast recognition of the target in the video sequence. The experimental results show that the proposed algorithm can overcome the influence of environmental interference and target occlusion on the tracking effect, reducing the target tracking error recognition rate to 0.156%. The computational time cost is 30 frames per second, which is 3.2 frames per second greater than the frame rate of the improved SiamMask model and 11.94% greater efficiency than that of the original SiamMask model. The algorithm meets the requirements of accuracy and real-time performance of the visual recognition and early warning system, and has reference significance for the application of the moving target recognition algorithm model in a complex environment.