Vehicle Thermal Pedestrian Detection Method Based On Pedestrian Subclasses

Vehicle-mounted thermal imaging pedestrian detection system obtains scene information through thermal imaging sensor and use pattern recognition technology to detect pedestrians in road scenes.Most of the existing methods treat image pedestrian and background as two-class classification problems.The road scene is dynamic.Pedestrians are non-rigid and their appearance is changeable.The classification boundary of pedestrian samples and background samples is nonlinear,so it is difficult for linear classifier to accurately classify pedestrians and background in the road scene.In this paper,the data distribution is analyzed,and subclasses are adopted to reduce the intra-row differences.Based on this,the data distribution and classifier structure are optimized to improve the performance of vehicle-mounted thermal imaging pedestrian detection model.1.The sensor angle and pedestrian attitude change continuously,the pedestrian diversity in the road scene is significant,and the difference within the sample class is large.Based on pedestrian characteristics,pedestrian subclasses are subclassed,and the efficiency of artificial subclasses is limited.The clustering of high-dimensional data is difficult to converge,so manifold learning is used to map high-dimensional pedestrian characteristics to low-dimensional expressions on the premise of preserving pedestrian structural characteristics.2.The experiment shows that the imbalance of samples of each subclass affects the performance of the deep convolutional network model,and the data balance method is designed for the thermal imaging pedestrian to promote the model to learn the common characteristics of different pedestrian appearances.Resampling is used to balance the number of pedestrian subclasses.In order to prevent the over-fitting of the model after resampling,the case switching strategy of thermal imaging image was designed to enhance the diversity of pedestrian samples.The optimization loss function adjusts the learning weight of each pedestrian subclass.The experimental results show that the detection accuracy of the model is improved.3.System-level integration optimization.Multiple base classifiers were obtained by subclass training,and a cascade classifier was constructed by integrating base classifier scores according to classifier weights.Improve RoIs screening mechanism,adopt score attenuation and local sliding window mechanism,reduce the rate of missed detection,reduce false alarm.Experiments show that the performance of cascade classifier is close to that of kernel function SVM and the computation is lower,which is suitable for embedded applications such as DSP.