Joint operator detection and tracking for person following from mobile platforms

In this paper, we propose an integrated system to detect and track a single operator that can switch off and on when it leaves and (re-)enters the scene. Our method is based on a set-valued Bayes-optimal state estimator that integrates RGB-D detections and image-based classification to improve tracking results in severe clutter and under long-term occlusion. The classifier is trained in two stages: First, we train a deep convolutional neural network to obtain a feature representation for person re-identification. Then, we bootstrap a classifier that discriminates the operator from remaining people on the output of the state-estimator. We evaluate the approach on a publicly available multi-target tracking dataset as well as custom datasets that are specific to our problem formulation. Experimental results suggest reliable tracking accuracy in crowded scenes and robust re-detection after long-term occlusion.