Abstract: Automatic identification of irrelevant individuals aims to detect and identify irrelevant persons in videos to solve their privacy protection issues.Existing privacy protection methods extract high-level visual features to identify individuals irrelevant to the subject.However,the extraction of high-level features significantly affects the processing efficiency of the video and makes it difficult to process massive video data.At the same time,the existing single-frame recognition method does not consider the temporal characteristics of the target,resulting in low accuracy.Therefore,this paper proposes an automatic recognition algorithm to efficiently identify irrelevant individuals,and introduces a multi-target tracking method to determine the correlation between people and videos.The method can extract five lightweight features from the time and space dimensions of the individual’s motion trajectory.In addition,in order to solve the challenges brought by occlusion and blur during video motion,an observation-based trajectory association algorithm is adopted to improve the accuracy of motion tracking.Extensive experiments conducted on various datasets demonstrate that the proposed method achieves significant improvements across multiple evaluation metrics compared to state-of-the-art approaches.Specifically,the MOTA metric shows a maximum improvement of 10.87 percentage points,the HOTA me-tric achieves a maximum increase of 10.95 percentage points,and the accuracy of irrelevant individuals recognition reaches 98.13%.

Key words: Irrelevant individuals recognition, Privacy protection, Multi-object tracking, Face detection, Deep learning