Dense Multiperson Tracking with Robust Hierarchical Linear Assignment

We introduce a novel dual-stage algorithm for online multi-target tracking in realistic conditions. In the first stage, the problem of data association between tracklets and detections, given partial occlusion, is addressed using a novel occlusion robust appearance similarity method. This is used to robustly link tracklets with detections without requiring explicit knowledge of the occluded regions. In the second stage, tracklets are linked using a novel method of constraining the linking process that removes the need for ad-hoc tracklet linking rules. In this method, links between tracklets are permitted based on their agreement with optical flow evidence. Tests of this new tracking system have been performed using several public datasets.

Adaptive NormalHedge for robust visual tracking

In this paper, we propose a novel visual tracking framework, based on a decision-theoretic online learning algorithm namely NormalHedge. To make NormalHedge more robust against noise, we propose an adaptive NormalHedge algorithm, which exploits the historic information of each expert to perform more accurate prediction than the standard NormalHedge. Technically, we use a set of weighted experts to predict the state of the target to be tracked over time. The weight of each expert is online learned by pushing the cumulative regret of the learner towards that of the expert. Our simulation experiments demonstrate the effectiveness of the proposed adaptive NormalHedge, compared to the standard NormalHedge method. Furthermore, the experimental results of several challenging video sequences show that the proposed tracking method outperforms several state-of-the-art methods.

Efficient tracking of human poses using a manifold hierarchy

In this paper a 3D human pose tracking framework is presented. A new dimensionality reduction method (Hierarchical Temporal Laplacian Eigenmaps) is introduced to represent activities in hierarchies of low dimensional spaces. Such a hierarchy provides increasing independence between limbs, allowing higher flexibility and adaptability that result in improved accuracy. Moreover, a novel deterministic optimisation method (Hierarchical Manifold Search) is applied to estimate efficiently the position of the corresponding body parts. Finally, evaluation on public datasets such as HumanEva demonstrates that our approach achieves a 62.5mm-65mm average joint error for the walking activity and outperforms state-of-the-art methods in terms of accuracy and computational cost.