Segmentation of Moving Objects in Traffic Video Datasets

In this paper, we aim to automate segmentation of multiple moving objects in video datasets specific to traffic use case. This automation is achieved in two steps. First, we generate bounding boxes using our proposed multi-object tracking algorithm based on convolutional neural network (CNN) model which is capable of re-identification. Second, we convert the various tracked objects into pixel masks using an instance segmentation algorithm. The proposed method of tracking has shown promising results with high precision and success rate in traffic video datasets specifically when there is severe object occlusion and frequent camera motion present in the video. Generating instance aware pixel masks for multiple object instances of a video dataset for ground truth is a tedious task. The proposed method offers interactive corrections with human-in-the-loop to improve the bounding boxes and the pixel masks as the video sequence proceeds. It exhibits powerful generalization capabilities and hence the proposed tracker and segmentation network was applied as a part of an annotation tool to reduce human effort and time.