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Utilizing surveillance cameras for passive visual positioning is at the forefront of indoor security research. Various methods have significantly improved the accuracy of passive visual positioning for pedestrians. Nevertheless, the long-distance error of passive vision has recently garnered attention as a critical issue. To address this problem, we propose a novel method named BIPSG, comprising forward propagation and backward propagation processes. In the forward propagation process, we propose a method for constructing the dynamic constraint region model. This method fuses passive visual positioning results with pedestrian detection box information to obtain the model required for geomagnetism in backward propagation, completing the forward propagation of information from passive vision to geomagnetism. In the backward propagation process, firstly, we propose a connected features amendment-based geomagnetic positioning method, using the constructed dynamic constraint region model as a constraint for geomagnetic matching and connected features extraction, subsequently amending matching results by connected features to reduce the probability of geomagnetic mismatching. Then, amended geomagnetic positioning results are fused with passive vision to reduce the long-distance error. The performance of the method was evaluated using the most common scene captured by an indoor surveillance camera. The experimental results show that our BIPSG method has reduced the average positioning error by 41.67% and the root mean square error by 31.87%, compared to the state-of-the-art method proposed in our previous research. The proposed method can effectively reduce the long-distance error of passive vision, achieving outstanding positioning accuracy. Additionally, pedestrian trajectories demonstrate the stability and continuity of the positioning.
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