School of Electrical Engineering and Computer Science, Gwanju Institute of Science and Technology, Gwangju 61005, Korea.
School of Engineering, RMIT University, Melbourne, VIC 3000, Australia.
Sensors (Basel). 2019 Jan 29;19(3):559. doi: 10.3390/s19030559.
With recent advances in object detection, the tracking-by-detection method has become mainstream for multi-object tracking in computer vision. The tracking-by-detection scheme necessarily has to resolve a problem of data association between existing tracks and newly received detections at each frame. In this paper, we propose a new deep neural network (DNN) architecture that can solve the data association problem with a variable number of both tracks and detections including false positives. The proposed network consists of two parts: encoder and decoder. The encoder is the fully connected network with several layers that take bounding boxes of both detection and track-history as inputs. The outputs of the encoder are sequentially fed into the decoder which is composed of the bi-directional Long Short-Term Memory (LSTM) networks with a projection layer. The final output of the proposed network is an association matrix that reflects matching scores between tracks and detections. To train the network, we generate training samples using the annotation of Stanford Drone Dataset (SDD). The experiment results show that the proposed network achieves considerably high recall and precision rate as the binary classifier for the assignment tasks. We apply our network to track multiple objects on real-world datasets and evaluate the tracking performance. The performance of our tracker outperforms previous works based on DNN and comparable to other state-of-the-art methods.
随着目标检测技术的最新进展，基于检测的跟踪方法已成为计算机视觉中多目标跟踪的主流方法。基于检测的跟踪方案必须解决在每一帧中现有跟踪和新接收检测之间的数据关联问题。在本文中，我们提出了一种新的深度神经网络（DNN）架构，该架构可以解决包括误报在内的具有可变数量的跟踪和检测的数据关联问题。所提出的网络由两部分组成：编码器和解码器。编码器是一个具有多个层的全连接网络，它将检测和跟踪历史的边界框作为输入。编码器的输出被顺序地输入到解码器中，解码器由具有投影层的双向长短期记忆（LSTM）网络组成。所提出的网络的最终输出是一个关联矩阵，反映了跟踪和检测之间的匹配分数。为了训练网络，我们使用斯坦福无人机数据集（SDD）的注释生成训练样本。实验结果表明，该网络作为分配任务的二进制分类器，实现了相当高的召回率和精度。我们将我们的网络应用于真实世界数据集上的多个对象的跟踪，并评估跟踪性能。我们的跟踪器的性能优于基于 DNN 的先前工作，并且与其他最先进的方法相当。
