Multi-Object Tracking Based on Segmentation and Collision Avoidance
Multi-Object Tracking Based on Segmentation and Collision Avoidance

An approach to track multiple objects in crowded scenes with long-term partial occlusions is proposed. Tracking-by-detection is a successful strategy to address the task of tracking multiple objects in unconstrained scenarios, but an obvious shortcoming of this method is that most information available in image sequences is simply ignored due to thresholding weak detection responses and applying non-maximum suppression. This paper proposes a multi-label conditional random field(CRF) model which integrates the superpixel information and detection responses into a unified energy optimization framework to handle the task of tracking multiple targets. A key characteristic of the model is that the pairwise potential is constructed to enforce collision avoidance between objects, which can offer the advantage to improve the tracking performance in crowded scenes. Experiments on standard benchmark databases demonstrate that the proposed algorithm significantly outperforms the state-of-the-art tracking-by-detection methods.
An approach to track multiple objects in crowded scenes with long-term partial occlusions is proposed. Tracking-by-detection is a successful strategy to address the task of tracking multiple objects in unconstrained scenarios, but an obvious shortcoming of this method is that most information available in image sequences is simply ignored due to thresholding weak detection responses and applying non-maximum suppression. This paper proposes a multi-label conditional random field(CRF) model which integrates the superpixel information and detection responses into a unified energy optimization framework to handle the task of tracking multiple targets. A key characteristic of the model is that the pairwise potential is constructed to enforce collision avoidance between objects, which can offer the advantage to improve the tracking performance in crowded scenes. Experiments on standard benchmark databases demonstrate that the proposed algorithm significantly outperforms the state-of-the-art tracking-by-detection methods.