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End-to-end multiple object tracking (MOT) has been very popular recently in the field of computer vision. However, when performing the inter-frame data association, the majority of algorithms ignore the diversity among trajectories and treat them uniformly. In the joint-detection-and-tracking (JDT) paradigm, there exists an optimization conflict between detection and re-identification due to their different optimization directions. In this paper, we propose a dynamic trajectory quantification (DTQ) strategy for MOT with feature rearrangement. Initially, the DTQ strategy quantifies each trajectory to show its quality, and the score is renewed every frame. To adapt to different situations, we dynamically adopt different updating mechanisms. Additionally, we provide a channel-enhanced feature rearrangement module to alleviate the optimization conflict between subtasks in the JDT paradigm, which obtains more convincing detection results for the DTQ strategy. We evaluate our proposed tracker on several benchmarks, i.e., MOT15, MOT16, MOT17, and MOT20. Extensive experimental results demonstrate that our method achieves competitive results while still maintaining a sizable real-time tracking speed. [ABSTRACT FROM AUTHOR]

The skeleton-based action recognition task has recently drawn much attention in computer vision, and graph convolutional networks (GCNs) have shown great advantages in this task. However, in shallow layers of GCNs, the sharing of convolution kernels in regular graph convolution (RGC) may result in the underutilization of some convolution kernels due to the particularity of the skeleton data. To capture richer locally discriminative action features, we propose the exclusive kernel graph convolution (EKGC) for the shallow layers of GCNs, which assigns the vertexes exclusive convolution kernels for graph convolution. At the same time, following the mixed graph convolution strategy, RGC is retained in the deep layers of the network, and the mixed graph convolutional network (MGCN) is proposed to utilize EKGC and RGC reasonably in all layers of the network. In addition, as a new form of skeleton information in GCN-based methods, angle information is explored in our work. Compared with joint data and bone data, angle data have better invariance against rotation and scaling, and they expand the relationships between skeletons and enrich the data representation of action. Finally, our final model, the multi-stream MGCN, is validated on two challenging datasets, achieving the state-of-the-art performance on the NTU-RGBD dataset and making competitive performance on a kinetics-skeleton dataset. [ABSTRACT FROM AUTHOR]