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Single object tracking based on Spatio-Temporal information
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DOI：10.1016/j.image.2025.117463.png)
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Abstract
En 中文
To address the challenge of tracking difficulties due to the absence of temporal dynamic information and background clutter interference caused by similar backgrounds, similar objects, target occlusion, and illumination changes during target tracking, this paper proposes a single object tracking algorithm based on spatio-temporal information (SST). The algorithm integrates a Temporal Adaptive Module (TAM) into the backbone network to generate a temporal kernel based on feature maps. This endows the network with the capability to model temporal dynamics, effectively utilizing the temporal relationships between frames to handle complex temporal dynamics such as changes in target motion states and environmental conditions. Additionally, to mitigate background clutter interference, the algorithm employs a Mixed Local Channel Attention (MLCA) mechanism, which captures channel and spatial information to focus the network on the target and reduce the impact of interfering information. The proposed algorithm was evaluated on OTB100, LaSOT, and NFS datasets. It achieved an AUC score of 70.7% on OTB, which represents a 1.3% improvement over the baseline tracker. On LaSOT and NFS datasets, it obtained AUC scores of 65.1% and 65.9%, respectively, showing improvements of 0.2% compared to the baseline tracker. The tracking speed exceeds 80fps, and the performance of the SST algorithm has been verified on self-made videos. The code is available at https://github.com/xuexiaodemenggubao/sst.
Keywords:
Temporal information
Spatial information
Attention mechanism
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