基于MobileNet的多目标跟踪深度学习算法

Deep learning algorithm based on MobileNet for multi-target tracking

针对深度学习算法在多目标跟踪中的实时性问题,提出一种基于MobileNet的多目标跟踪算法.借助于MobileNet深度可分离卷积能够对深度网络模型进行压缩的原理,将YOLOv3主干网络替换为MobileNet,通过将标准卷积分解为深度卷积和逐点卷积,保留多尺度预测部分,以有效减少参数量.对于检测得到的边框信息,利用Deep-SORT算法进行跟踪.实验结果表明,所提出方法在跟踪效果基本不变的情况下可提升处理速度近50%.

For the real-time problem of deep learning algorithm in multi-target tracking,a multi-target tracking algorithm based on MobileNet is proposed.Using the principle that deepwise separable convolution of MobileNet can compress deep network models,the YOLOv3 backbone network is replaced by MobileNet,but multiscale predictions are remained,which reduces effectively the amount of parameters by factorizing a standard convolution into a depthwise convolution and a pointwise convolution.For the detected bounding box information,the tracking task is performed using the Deep-SORT algorithm.The experimental results show that the processing speed is improved by nearly 50%under the condition of maintaining the tracking result.