基于改进MDNet的视频目标跟踪算法

目标跟踪算法面对的突出问题之一是正负样本不均衡, 正样本极度相似. 针对跟踪更新过程中正样本不丰富的问题, 本文基于多域卷积神经网络(MDNet)算法, 提出了一种改进MDNet的视频目标跟踪算法, 首先改进原算法中候选框的选取策略, 提出了一种基于候选框置信度与坐标方差阈值判断相结合的模型更新方法, 其次将原算法的交叉熵损失函数改进为效果更好的focal loss损失函数. 实验结果表明在相同实验环境下本文算法相较于MDNet算法在跟踪准确率和成功率上均有明显提高.

Video Object Tracking Algorithm Based on Improved MDNet

One of the major problems of the object tracking algorithm is the imbalance of positive and negative samples, and the positive samples are of high similarity. Aiming at the problem of insufficient positive samples in the tracking update process, this study proposes an improved MDNet-based video object tracking algorithm based on the multi-domain convolutional neural network (MDNet) algorithm. First, the strategy of candidate selection is improved in the original algorithm, and a model update method is presented on the basis of the combination of the candidate confidence and the threshold judgment of coordinate variance. Second, the cross-entropy loss function of the original algorithm is altered to a focal loss function with better performance. The experimental results show that the algorithm has a significant improvement in tracking precision and success rate compared with the MDNet algorithm under the same experimental environment.