Deep Learning Target Tracking Algorithm Based on Construction Site Scene
MA Shao-xiong1,2, QIU Shi3, TANG Ying4, ZHANG Xiao5
1. Xi'an University of Technology, Xi'an, Shaanxi 710048, China;
2. Shaanxi Railway Institute, Weinan, Shaanxi 714000, China;
3. Key Laboratory of Spectral Imaging Technology CAS, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, Shaanxi 710119, China;
4. Chengdu University of Technology, Chengdu, Sichuan 610059, China;
5. Northwest University, Xi'an, Shaanxi 710127, China
Abstract:Construction site is difficult to be effectively managed owing to its complex environment.A deep learning target tracking algorithm based on construction site scene is proposed to assist the construction progress.Firstly,according to the continuity of the target in the site scene,the enhanced group tracker is constructed to improve the successful probability of target tracking.Then,the depth detector is constructed with sliding window,stacked denoising auto encoder (SDAE) and support vector machine (SVM).Sliding window:a model is built from the gradient angle to realize window adaption.SDAE algorithm:the reverse algorithm is built to fine-tune network parameters.Optimized SVM algorithm reduces the probability of target drift and tracking failure.Finally,high precision tracking is achieved.Experiments show that the proposed algorithm can track the target effectively and realize dynamic management.
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