A Semi-Automatic Video Labeling Tool for Autonomous Driving Based on Multi-Object Detector and Tracker

In recent years, the development of autonomous cars has gained great momentum due to the vast advances in deep learning technique for recognizing and tracking objects on the roads. To apply the deep learning technique, a large set of properly annotated videos are normally needed to train the neuron network model. However, the process of annotating videos is very time-consuming and tedious, and currently it relies mainly on human. Automating this process is actually a chicken-and-egg problem: we need a perfect object detection and tracking tool to annotate the videos so as to train a perfect object detection and tracking algorithm. A viable alternative is to annotate the videos using a less perfect tool and then correct the results manually. In this paper, we introduce such a semi-automatic video labeling tool for autonomous driving. Our tool is based on the open-source video annotation system VATIC. A multi-object detector and tracker is first used to annotate the video. Possible errors in the labeling of the objects are identified and then presented to human annotators to produce correct annotations. Experiments on labeling test videos show that our tool can complete the annotation task faster, while maintaining the same quality as a human annotator. The proposed tool is modified from the open-source tool VATIC and is available from Github.