Pedestrian detection method based on self-learning

Vision-based pedestrian detection technology is widely used in security surveillance, such as transportation, prisons, and important places. Security monitoring requires pedestrian detection not only to be accurate and reliable, but also to meet the needs of real-time monitoring, and the false alarm rate is low. The existing pedestrian learning method based on deep learning has high reliability, but the timeliness cannot meet the monitoring requirements. Traditional pedestrian detection methods are time-sensitive, but false alarm rates tend to be higher. This paper proposes a self-learning pedestrian detection method, combined with motion detection, HOG feature extraction, and SVM classification for preliminary pedestrian detection. The false alarms caused during the pedestrian detection process are manually confirmed, and then the system automatically detects the false alarm image at a specific time. Self-learning, retraining the classifier, so as to continuously reduce the pedestrian detection false alarm rate and improve the pedestrian detection performance index through self-learning during the system operation, and the timeliness can meet the needs of video surveillance. Experiments show that the false alarm rate of this method is low, timeliness is high, and the recognition rate is also high.