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It has been recognised that a model predictive control (MPC) approach can be successfully applied to many areas in the signal control of urban traffic networks. In this study, the authors propose a novel distributed MPC algorithm with decomposition of the signal control problem in the urban traffic networks. The objective of each subsystem takes into account the information of neighbouring subsystems, being this way affected by their non-local decisions. The proof is also given to show that the solution of the new approach will converge to a global optimum after adequate iterations. Moreover, the simulation result shows that our method solves the problem with an acceptable solution performance.

Visual surveillance using multiple cameras has attracted increasing interest in recent years. Correspondence between multiple cameras is one of the most important and basic problems which visual surveillance using multiple cameras brings. In this paper, we propose a simple and robust method, based on principal axes of people, to match people across multiple cameras. The correspondence likelihood reflecting the similarity of principal axis pairs is constructed according to the relationship between "ground-points" detected in each camera view and the intersections of principal axes detected in different camera views and transformed to the same view. Our method has the following desirable properties: (1) Camera calibration is not needed. (2) Accurate motion detection and segmentation are less critical due to the robustness of the principal axis-based feature to noise. (3) Based on the fused data derived from correspondence results, positions of people in each camera view can be accurately located even when the people are partially occluded in all views. The experimental results on several real video sequences from outdoor environments have demonstrated the effectiveness, efficiency and robustness of our method.

Axially chiral indole-based frameworks occur in natural products, bioactive molecules, and chiral ligands. Thus, the development of catalytic asymmetric atroposelective approaches for de novo construction of these frameworks is highly valuable. Here, the atroposelective organo/metal combined dual catalysis strategy for de novo construction of valuable axially chiral indole frameworks has been developed. This protocol utilized a catalyst system of two chiral phosphoric acids (1 mol %) in combination with AgNO3 (1 mol %) and was based on the unreported intermolecular cycloaddition–isomerization reaction of our recently introduced C-alkynyl N,O-acetals and 2-naphthylamines. An important class of hitherto inaccessible axially chiral indoles with a C–N axis were obtained in good yields and enantioselectivities. The axially chiral indoles obtained also provided a platform for the catalyst-controlled atroposelective synthesis of axially chiral indoles bearing two C–N axes, which are difficult to access by the existing methods. This work is also an example of 2-naphthylamines used as 1,3-dinucleophiles and three-atom (CCN) synthons in cycloadditions.

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The one-shot multi-object tracking, which integrates object detection and ID embedding extraction into a unified network, has achieved groundbreaking results in recent years. However, current one-shot trackers solely rely on single-frame detections to predict candidate bounding boxes, which may be unreliable when facing disastrous visual degradation, e.g., motion blur, occlusions. Once a target bounding box is mistakenly classified as background by the detector, the temporal consistency of its corresponding tracklet will be no longer maintained. In this paper, we set out to restore the bounding boxes misclassified as ``fake background'' by proposing a re-check network. The re-check network innovatively expands the role of ID embedding from data association to motion forecasting by effectively propagating previous tracklets to the current frame with a small overhead. Note that the propagation results are yielded by an independent and efficient embedding search, preventing the model from over-relying on detection results. Eventually, it helps to reload the ``fake background'' and repair the broken tracklets. Building on a strong baseline CSTrack, we construct a new one-shot tracker and achieve favorable gains by 70.7 ➡ 76.4, 70.6 ➡ 76.3 MOTA on MOT16 and MOT17, respectively. It also reaches a new state-of-the-art MOTA and IDF1 performance. Code is released at https://github.com/JudasDie/SOTS.