Research Group for Pattern Recognition, University of Siegen, Hölderlinstr. 3, 57076 Siegen, Germany.
Sensors (Basel). 2018 Nov 16;18(11):3994. doi: 10.3390/s18113994.
Object tracking in challenging videos is a hot topic in machine vision. Recently, novel training-based detectors, especially using the powerful deep learning schemes, have been proposed to detect objects in still images. However, there is still a semantic gap between the object detectors and higher level applications like object tracking in videos. This paper presents a comparative study of outstanding learning-based object detectors such as ACF, Region-Based Convolutional Neural Network (RCNN), FastRCNN, FasterRCNN and You Only Look Once (YOLO) for object tracking. We use an online and offline training method for tracking. The online tracker trains the detectors with a generated synthetic set of images from the object of interest in the first frame. Then, the detectors detect the objects of interest in the next frames. The detector is updated online by using the detected objects from the last frames of the video. The offline tracker uses the detector for object detection in still images and then a tracker based on Kalman filter associates the objects among video frames. Our research is performed on a TLD dataset which contains challenging situations for tracking. Source codes and implementation details for the trackers are published to make both the reproduction of the results reported in this paper and the re-use and further development of the trackers for other researchers. The results demonstrate that ACF and YOLO trackers show more stability than the other trackers.
目标跟踪是机器视觉领域的一个热点研究问题。最近，基于新型训练的检测器，特别是使用强大的深度学习方法，已被提出用于检测静态图像中的目标。然而，在目标检测和视频中的目标跟踪等更高层次的应用之间，仍然存在语义鸿沟。本文对基于学习的杰出目标检测器，如 ACF、基于区域的卷积神经网络（RCNN）、FastRCNN、FasterRCNN 和 You Only Look Once（YOLO），进行了比较研究。我们使用在线和离线训练方法进行跟踪。在线跟踪器使用从第一帧中感兴趣的目标生成的一组合成图像来训练检测器。然后，检测器在接下来的帧中检测感兴趣的目标。通过使用视频最后几帧中检测到的对象，检测器在在线上进行更新。离线跟踪器使用检测器在静态图像中进行目标检测，然后基于卡尔曼滤波器的跟踪器将对象关联到视频帧之间。我们的研究是在 TLD 数据集上进行的，该数据集包含跟踪的挑战性情况。我们发布了跟踪器的源代码和实现细节，以便重现本文报告的结果，并为其他研究人员重新使用和进一步开发跟踪器。结果表明，ACF 和 YOLO 跟踪器比其他跟踪器更稳定。
