Online tracking using saliency

When tracking small moving objects, primates use smooth pursuit eye movements to keep a target in the center of the field of view. In this paper, we propose the Smooth Pursuit tracking algorithm, which uses three kinds of saliency maps to perform online target tracking: appearance, location, and motion. In addition to tracking single targets, our method can track multiple targets with little additional overhead. The appearance saliency map uses deep convolutional neural network features along with gnostic fields, a brain-inspired model for object recognition. The location saliency map predicts where the object will move next. Finally, the motion saliency map indicates which objects are moving in the scene. We combine all three saliency maps into a smooth pursuit map, which is used to generate bounding boxes for tracked objects. We evaluate our algorithm and others from the literature on a vehicle tracking task. Our approach achieves the best overall performance, including being the only method we tested capable of handling long-term occlusions.