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Considering the shortcomings of traditional video multi-target recognition technology in rapidly identifying criminal suspects in complex scenes, a multi-target recognition optimization method based on video tracking technology is proposed. This method constructs a multi-target recognition algorithm based on video feature matching, and introduces the Kalman filter algorithm to improve the accuracy and real-time recognition of criminal suspects through the definition of feature vector and similarity function. Experiments showed that the model proposed in the study performed exceptionally well in terms of tracking error; the highest precision was 94.75%, the recall rate was 96.59%, the tracking error of the horizontal axis was only 3.75%, and the tracking error of the vertical axis was 3.27%. In the crime detection video application, the accuracy–recall curve of the model was 0.94, and the feature recall rate was 94.83%, verifying the effectiveness and robustness of the model in complex and fast scenes. The results show that the proposed model has good feasibility and robustness in rapidly identifying criminal suspects. In addition, the work offered new technical concepts for improving target tracking precision and adapting to real-time scene changes, opening new research avenues in the field of multi-target recognition.

Optimization of multi-objective recognition based on video tracking technology