Current Issue Cover
自适应卷积特征选择的实时跟踪算法(NCIG2018)

熊昌镇,车满强,王润玲(北方工业大学)

摘 要
为减少目标跟踪卷积特征的冗余性,提高跟踪算法的实时性和鲁棒性,提出了一种自适应卷积特征选择的实时跟踪算法。该算法利用目标区域和搜索区域特征的均值比来评估卷积操作,先选取满足均值比阈值的特征通道数最多的卷积层,然后提取该卷积的有效卷积特征来训练相关滤波分类器,最后采用稀疏的模型更新策略提高跟踪速度。在OTB-100标准数据集上进行算法测试,实验结果表明,本文算法平均距离精度值达86.4%,平均跟踪速度达29.9帧/秒,比分层卷积相关滤波跟踪算法平均距离精度值提高了2.7个百分点,速度快将近3倍。
关键词
Adaptive convolutional feature selection for real-time visual tracking

Xiong Changzhen,Che Manqiang,Wang Runling(Beijing Key Laboratory of Urban Intelligent Control,Beijing;College of Sciences,North China University of Technology,Beijing)

Abstract
In order to reduce the redundancy of convolution feature for visual tracking and improve the real-time and robustness of the visual tracking algorithm, a real-time tracking algorithm based on adaptive convolutional features selection is proposed. The algorithm uses the feature mean ratio of the object region and the search region to evaluate the convolution operator. Firstly, the convolutional layer with the largest number of convolutional channels satisfying the feature mean ratio threshold is selected, and then the effective convolutional features of the selected convolutional layer is extracted to train the correlation filter classifier. Finally a sparse model updating strategy is adopted to improve the tracking speed. The algorithm is tested on OTB-100 standard dataset. The experimental results show that the average distance accuracy of this algorithm is 86.4%, 2.7% higher than the original hierarchical convolutional features algorithm. the average tracking speed is 29.9 frames / sec, which is almost 3 times faster than before.
Keywords
QQ在线


订阅号|日报