New effective treatment of the light-front nonvalence contribution in timelike exclusive processes

We discuss a necessary nonvalence contribution in timelike exclusive processes. Following a Schwinger-Dyson type of approach, we relate the nonvalence contribution to an ordinary light-front wave function that has been extensively tested in the spacelike exclusive processes. A complicate four-body energy denominator is exactly cancelled in summing the light-front time-ordered amplitudes. Applying our method to $K_{\ell3}$ and $D^0\to K^- \ell^+ \nu_l$ where a rather substantial nonvalence contribution is expected, we find not only an improvement in comparing with the experimental data but also a covariance(i.e. frame-independence) of existing light-front constituent quark model.Comment: 10 pages including 5 figures; Changes: 1-added some sentences; 2-enlarged the figures; 3-added some reference

Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor

In this paper, we focus on the two key aspects of multiple target tracking problem: 1) designing an accurate affinity measure to associate detections and 2) implementing an efficient and accurate (near) online multiple target tracking algorithm. As the first contribution, we introduce a novel Aggregated Local Flow Descriptor (ALFD) that encodes the relative motion pattern between a pair of temporally distant detections using long term interest point trajectories (IPTs). Leveraging on the IPTs, the ALFD provides a robust affinity measure for estimating the likelihood of matching detections regardless of the application scenarios. As another contribution, we present a Near-Online Multi-target Tracking (NOMT) algorithm. The tracking problem is formulated as a data-association between targets and detections in a temporal window, that is performed repeatedly at every frame. While being efficient, NOMT achieves robustness via integrating multiple cues including ALFD metric, target dynamics, appearance similarity, and long term trajectory regularization into the model. Our ablative analysis verifies the superiority of the ALFD metric over the other conventional affinity metrics. We run a comprehensive experimental evaluation on two challenging tracking datasets, KITTI and MOT datasets. The NOMT method combined with ALFD metric achieves the best accuracy in both datasets with significant margins (about 10% higher MOTA) over the state-of-the-arts