Entanglement inspired approach for determining the preeminent arrangement of static cameras in a multi-view computer vision system

This paper is on the concept of quantum steering and quantum entanglement of two observers. The concept is applied to a multi-view computer vision system that incorporates two cameras. Three separate multi-view static camera setups are used to compare the recognition accuracy and to improve the arrangement of cameras in gesture recognition and classroom organisation applications. Prominent features which are partially hidden from a viewpoint can increase performance if given attention. In view of that, this paper proposes an entanglement-based ranking technique that updates the weights of attentive features to improve the classification rate. Principles of entanglement are also used to optimise the position of cameras such that the authoritative hidden features are visible. The proposed technique has a high recognition rate with static cameras. It also shows a low error rate in the field of view when switching to other applications. The results are validated with derangement and Bland-Altman agreement test. The entanglement approach for determining the fine-tuned position of static cameras in a recognition task outperforms many active camera networks.

Automated summary

This paper explores the concepts of quantum steering and quantum entanglement between two observers, and applies them to a multi-view computer vision system. By comparing the recognition accuracy of multiple cameras, the arrangement of cameras in gesture recognition and classroom organization applications is improved. Additionally, an entanglement-based ranking technique is proposed to enhance the classification rate, and the principles of entanglement are used to optimize the camera position for visibility of hidden features. Experimental results demonstrate that this approach achieves high recognition rates with static cameras and low error rates when switching to other applications.