Cascade Human Detection Based On Deformable Part Models

Human detection has been a research focus in the field of computer vision and pattern recognition, as times progressed, people’s life is becoming more and more rich and colorful with the rapid development of modern high-tech, many fields are involved in human detection, such as intelligent entrance guard system, the subway station. Both the human body detection system or the human body detection technology is portable to other platforms, human detection improved will naturally improve human detection system. So far, there are many human detection methods, but because the human body itself has more flexible variability and human environment is complex, human detection is still a challenging research topic. In recent years, the deformable component model shows more superiority in human detection, it can improve the efficiency of human detection using the relationship between the body’s overall information and part information, even the complex human body target or in the complex environment, the model can exhibit very good performance.As HOG features can exhibit strong robustness when light or shape changes and in complex environment, so we take it as the characteristics of training and detection of human target. After the extraction of body feature information, this paper use vector machine LSVM with potential values for the training of its classification, so as to obtain the detection model. In the establishment of deformable component model, according to the different contributions of the different body areas in the detecting effect, we set respective weights in different components, the component which has more response scores is more important to the detection process. On the basis of previous studies, the design enriches the marker information in the training samples, and reform the detection model to improve the detection performance. In view of the current problems of slow human detection rate, this paper combines the method of cascade detection, using the cascade model and the simplified model to replace the original model to simplify the detection process, the experimental results can greatly improve the detection speed without loss of accuracy. The experiment part is finally carried out in the INRIA database and own picture library, which achieved very good detection results and confirmed great detection performance.