Enhancing pedestrian detection accuracy: A full-stage refined proposal algorithm for false positive suppression

Pedestrian detection remains a critical component of autonomous driving and intelligent surveillance systems, providing essential perceptual information for their safe and reliable operation. However, false positives, where non-pedestrian objects or backgrounds are incorrectly classified as pedestrian objects, which can severely compromise normal system functionality. This problem is particularly challenging because pedestrians exhibit significant inter-class and intra-class appearance variations. Consequently, FPs persist as a fundamental and unsolved challenge. This paper addresses this problem by introducing a Full-stage Refined Proposal (FRP) algorithm aimed at eliminating FPs within a two-stage CNN-based pedestrian detection framework. Specifically, in the training stage, the Training mode FRP algorithm introduces a novel approach for validating pedestrian proposals to effectively guide the model training process, thereby constructing a model with strong capabilities for false positive suppression. During the inference stage, two innovative strategies are implemented: the Classifier-guided FRP algorithm integrates a pedestrian classifier into the proposal generation process to yield high-quality proposals through pedestrian feature evaluation, and the Split-proposal FRP algorithm vertically divides all proposals, sending both the original and the sub-region proposals to the subsequent subnetwork to evaluate their confidence scores, filtering out those with lower sub-region pedestrian confidence scores. As a result, the proposed algorithm enhances the model's ability to suppress pedestrian false positives across all stages. Experiments on benchmarks and the SY-Metro dataset demonstrate the FRP algorithm's effectiveness in suppressing FPs, with better improvements in detection accuracy. For instance, on CityPersons, the full-mode FRP lowers the log-average miss rate from 71.87% to 69.27% (a 2.6% absolute improvement) over the plain MetroNext, at the cost of only 5 ms/frame additional latency on GPU. Deployment experiments on embedded platforms, such as the Jetson Nano, highlight the FRP algorithm's potential for real-world applications, offering a competitive solution for pedestrian detection in resource-constrained environments. The source code for this paper is available at https://github.com/guoqiang0148666/fsrp-algorithm .