Video tracking

Convolutional neural network

Artificial intelligence

Embedding

Transformer

Physics

Pattern recognition (psychology)

Image (mathematics)

Quantum mechanics

Operating system

Voltage

Computer science

Minimum bounding box

Laptop

Encoder

Object (grammar)

Computer vision

In this paper, we present a new tracking architecture with an encoder-decoder transformer as the key component. The encoder models the global spatio-temporal feature dependencies between target objects and search regions, while the decoder learns a query embedding to predict the spatial positions of the target objects. Our method casts object tracking as a direct bounding box prediction problem, without using any proposals or predefined anchors. With the encoder-decoder transformer, the prediction of objects just uses a simple fully-convolutional network, which estimates the corners of objects directly. The whole method is end-to-end, does not need any postprocessing steps such as cosine window and bounding box smoothing, thus largely simplifying existing tracking pipelines. The proposed tracker achieves state-of-the-art performance on multiple challenging short-term and long-term benchmarks, while running at real-time speed, being 6× faster than Siam R-CNN [54]. Code and models are open-sourced at https://github.com/researchmm/Stark.

Learning Spatio-Temporal Transformer for Visual Tracking