Tracking The Untrackable: Learning To Track Multiple Cues with Long-Term Dependencies

Understand

The majority of existing solutions to the Multi-Target Tracking (MTT) problem do not combine cues in a coherent end-to-end fashion over a long period of time.

• However, we present an online method that encodes long-term temporal dependencies across multiple cues.
• One key challenge of tracking methods is to accurately track occluded targets or those which share similar appearance properties with surrounding objects.
• To address this challenge, we present a structure of Recurrent Neural Networks (RNN) that jointly reasons on multiple cues over a temporal window.