复杂环境下一种基于SiamMask的时空预测移动目标跟踪算法

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

随着无人工厂、智能安监等技术在制造业领域的深入应用,以视觉识别预警系统为代表的复杂环境下动态识别技术成为智能工业领域的重要研究内容之一.在本文所述的工业级视觉识别预警系统中,操作人员头发区域由于其具有移动形态非规则性、运动无规律性的特点,在动态图像中的实时分割较为困难.针对此问题,提出一种基于SiamMask模型的时空预测移动目标跟踪算法.该算法将基于PyTorch深度学习框架的SiamMask单目标跟踪算法与ROI检测及STC时空上下文预测算法相融合,根据目标时空关系的在线学习,预测新的目标位置并对SiamMask模型进行算法校正,实现视频序列中的目标快速识别.实验结果表明,所提出的算法能够克服环境干扰、目标遮挡对跟踪效果的影响,将目标跟踪误识别率降低至0.156%.该算法计算时间成本为每秒30帧,比改进前的SiamMask模型帧率每秒提高3.2帧,算法效率提高11.94%.该算法达到视觉识别预警系统准确性、实时性的要求,对移动目标识别算法模型的复杂环境应用具有借鉴意义.

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 Siam Mask model and 11.94%greater efficiency than that of the original Siam Mask 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.