Learning to Track Object Position through Occlusion
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Occlusion is one of the most significant challenges encountered by object detectors and trackers. While both object detection and tracking has received a lot of attention in the past, most existing methods in this domain do not target detecting or tracking objects when they are occluded. However, being able to detect or track an object of interest through occlusion has been a long standing challenge for different autonomous tasks. Traditional methods that employ visual object trackers with explicit occlusion modeling experience drift and make several fundamental assumptions about the data. We propose to address this with a `tracking-by-detection` approach that builds upon the success of region based video object detectors. Our video level object detector uses a novel recurrent computational unit at its core that enables long term propagation of object features even under occlusion. Finally, we compare our approach with existing state-of-the-art video object detectors and show that our approach achieves superior results on a dataset of furniture assembly videos collected from the internet, where small objects like screws, nuts, and bolts often get occluded from the camera viewpoint.
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