Abstract:

Background: Tracking wild animals sheds light on their ecology, behavior, and population dynamics. Developing an automated, effective, and precise target tracking system is crucial for the conservation of wild animals. Monitoring the Amur tiger (Panthera tigris altaica) population requires properly and automatically tracking individual tigers, which is challenging because of their camouflaged and fast movements in complex habitats. Individual Amur tigers are tracked incorrectly in the real world because of factors like similarity interference, occlusion, and illumination variation.
Methods: In order to accurately track Amur tigers in complex real-world circumstances, we have suggested a Siamese network tracking framework based on attention feature fusion. Through the incorporation of the attention feature fusion into the backbone network ResNet50, we have enhanced the conventional Siamese-based tracking architecture. With the addition of a multi-scale channel attention module, the system was better able to comprehend global contextual information and adjust to the varying environmental conditions and individual states of Amur tigers.
Results: The suggested approach was compared with five advanced algorithms, SiamFC, SiamRPN++, SiamCAR, SiamBAN and SiamGAT, on the Amur tiger target tracking dataset. The algorithm proposed in this paper achieved a tracking success rate of 72.5% and a precision of 93.9%, outperforming the five existing algorithms. It showed improvements of 4.1% and 2.3% compared to the baseline tracking algorithm. At the same time, the suggested approach performed better in tracking when faced with six distinct tracking problems in the Amur tiger’s complex environment.
Conclusion: This strategy significantly enhances the success rate and precision of tracking individual Amur tigers. The method in this paper is more suitable for the actual scene under the premise of using computer vision technology to monitor wild animals, and it proves that the tracking method in this paper can provide an effective scheme for automatic and efficient monitoring of Amur tigers in a complex actual scene.

Key words: Amur tiger, Single Object Tracking, Siamese network, Feature fusion, Attention mechanism