Multi-view pedestrian detection via residual mask fusion and cosine similarity-based passive sampler for video surveillance systems

Multi-view pedestrian detection aims to generate a bird’s-eye view occupancy map of pedestrians from multiple calibrated camera views. Multi-view methods offer advantages over single-view approaches: they can mitigate occlusions, expand scene coverage, and improve robustness. However, existing multi-view detection methods still face two critical challenges: mixing heterogeneous cross-view information in the fused representation and feature misalignment in the world coordinate system caused by various scales across views. To solve these issues, we develop a novel multi-view pedestrian detection framework that includes a residual mask fusion module and a cosine similarity-based passive sampler. Specifically, the residual mask fusion module enables adaptive feature selection and compensation across views, yielding an optimal fusion under geometric redundancy. Moreover, the cosine similarity-based passive sampler computes dynamic coordinate offsets by evaluating feature consistency. This reduces the impact of unavoidable biases introduced during projection. Experimental results on Wildtrack, MultiviewX and CityStreet demonstrate the effectiveness and reliability of the developed framework for multi-view pedestrian detection. Our code is available at https://github.com/guixiaojia/improve-shot .