Abstract
Multiple object tracking is an important but challenging computer vision problem. The complex motion of objects makes tracking difficult during long periods of object occlusion, and as a result occlusions frequently cause fragmented tracks with gaps. Previous works use linear interpolation to fill in such gaps, a technique which is only able to model simple motion. As a result, tracked bounding box locations can be quite poor in these situations. In this paper, we propose a 1D CNN based solution to filling gaps which models complex motion in a data-driven way. Our proposed solution uses only bounding box coordinates as input, and as such does not incur the computational cost of processing image features directly. We show that our model significantly outperforms linear interpolation on dynamic sports datasets in terms of mean intersection over union between predicted and ground truth bounding boxes.
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