Inducing Stronger Object Representations in Deep Visual Trackers

Ross Goroshin, Jonathan Tompson, Debidatta Dwibedi

Keywords: Object Tracking, Computer Vision, Deep Learning

Abstract:

Fully convolutional deep correlation networks are integral components of state-of-
the-art approaches to single object visual tracking. It is commonly assumed that
these networks perform tracking by detection by matching features of the object
instance with features of the entire frame. Strong architectural priors and conditioning
on the object representation is thought to encourage this tracking strategy.
Despite these strong priors, we show that deep trackers often default to “tracking-
by-saliency” detection – without relying on the object instance representation. Our
analysis shows that despite being a useful prior, salience detection can prevent the
emergence of more robust tracking strategies in deep networks. This leads us to
introduce an auxiliary detection task that encourages more discriminative object
representations that improve tracking performance.