An innovative unsupervised gait recognition based tracking system for safeguarding large-scale nature reserves in complex terrain

Chichun Zhou, Xiaolin Guan, Zhuohang Yu, Yao Shen, Zhenyu Zhang, Junjie Gu

Abstract:

Abnormal human activities play a significant role in triggering emergencies within vast nature reserves. The vast
area, complex terrain, and insufficient electricity and high-bandwidth network infrastructure present significant
challenges in effectively supervising nature reserves. Fortunately, intricate terrains often boast restricted access
points, typically confined to just a few narrow pathways and the gait recognition technique utilizes only a small
amount of binary-processed low-quality gait data and seamlessly integrates with low-resolution and low-powerconsumption
cameras making it particularly suitable for human activities supervision in nature reserves. However,
extensive existing supervised along with a limited number of unsupervised methods are unable to be
implemented in real-world application due to the reliance on the pre-labeled training set and the insufficient
retrieval accuracies. Here, we present an electronic tracking system for safeguarding large-scale nature reserves
in complex terrain based on the unsupervised gait recognition technique for the first time. 1) The proposed
method doesn’t require any known matching relationships in the training set. 2) It consistently achieves 100%
top-1 retrieval accuracies, with a distinct gap between the distances of top-1 and top-2 retrievals. This distinction
allows us to detect abnormal behaviors, such as individuals who enter without exiting, exit without entering, or
venture into restricted areas. It effectively mitigates the impact of human activities on the protected area at low
cost offering an application case of gait recognition technology (GRT) in the field of nature conservation.