In deep space environment, some objects may split into several small fragments during movement, and these deep space objects often appear as points in satellite images. In this article, we conduct research on multi-object tracking (MOT) for these objects. First, we propose a simulation dataset, ScatterDataset, which simulates the movement and separation of objects in deep space background. By assigning two IDs to a trajectory, we describe the trajectory’s relationship before and after separation. Second, we present an end-to-end motion association model, ScatterNet, which encodes the position information of trajectories and detections into motion features. These features are processed through temporal aggregation by a Transformer encoder and spatial aggregation by a graph network; then, we get the association results by calculating the similarity between these features. Finally, we introduce a tracker, ScatterTracker, which is suitable for tracking in scenarios with object separation. Experiments with state-of-the-art tracking methods on ScatterDataset demonstrate that our approach has achieved significant performance improvements in deep space scenarios. The code is available at:https://github.com/wht-bupt/ScatterTrack.

Compiling high-level programs to high-speed packet-processing pipelines is a challenging combinatorial optimization problem. The compiler must configure the pipeline’s resources to match the semantics of the program’s high-level specification, while packing all of the program’s computation into the pipeline’s limited resources. State of the art approaches tackle individual aspects of this problem. Yet, they miss opportunities to produce globally high-quality outcomes within reasonable compilation times. We develop a framework to decompose the compilation problem for such pipelines into three phases—making extensive use of solver engines (e.g., ILP, SMT, and program synthesis) to simplify the development of these phases. Transformation rewrites programs to use more abundant pipeline resources, avoiding scarce ones. Synthesis breaks complex transactional code into configurations of pipelined compute units. Allocation maps the program’s compute and memory to the pipeline’s hardware resources. We prototype these ideas in a compiler, CaT, which targets (1) the Tofino programmable switch pipeline and (2) Menshen, a cycle-accurate simulator of a Verilog description of the RMT pipeline. CaT can handle programs that existing compilers cannot currently run on pipelines and generates code faster than existing compilers, where the generated code uses fewer pipeline resources.

Mainstream multi-object tracking methods exploit appearance information and/or motion information to achieve interframe association. However, dealing with similar appearance and occlusion is a challenge for appearance information, while motion information is limited by linear assumptions and is prone to failure in nonlinear motion patterns. In this work, we disregard appearance clues and propose a pure motion tracker to address the above issues. It dexterously utilizes Transformer to estimate complex motion and achieves high-performance tracking with low computing resources. Furthermore, contrastive learning is introduced to optimize feature representation for robust association. Specifically, we first exploit the long-range modeling capability of Transformer to mine intention information in temporal motion and decision information in spatial interaction and introduce prior detection to constrain the range of motion estimation. Then, we introduce contrastive learning as an auxiliary task to extract reliable motion features to compute affinity and introduce bidirectional matching to improve the affinity computation distribution. In addition, given that both tasks are dedicated to narrowing the embedding distance between the motion features of the tracked object and the detection features, we design a joint-motion-and-association framework to unify the above two tasks in one framework for optimization. The experimental results achieved with three benchmark datasets, MOT17, MOT20 and DanceTrack, verify the effectiveness of our proposed method. Compared with state-of-the-art methods, the proposed STDFormer sets a new state-of-the-art on DanceTrack and achieves competitive performance on MOT17 and MOT20. This demonstrates the advantage of our method in handling associations under similar appearance, occlusion or nonlinear motion. At the same time, the significant advantages of the proposed method over Transformer-based and contrastive learning-based methods suggest a new direction for the application of Transformer and contrastive learning in MOT. In addition, to verify the generalization of STDFormer in unmanned aerial vehicle (UAV) videos, we also evaluate STDFormer on VisDrone2019. The results show that STDFormer achieves state-of-the-art performance on VisDrone2019, which proves that it can handle small-scale object associations in UAV videos well. The code is available at https://github.com/Xiaotong-Zhu/STDFormer.