The R&D Of A Logistics Data Collection System Based On Images

Logistics data collection is the foundation of achieving supply chain logistics informatization. Based on image processing method, this paper makes logistics data collection and statistical analysis by taking the pedestrian as the moving target of image research. On the basis of moving target detection, classification and tracking technologies,this paper designs and realizes a logistics data collection system based on images,combining the wide application of target statistics in public places, such as bus stations, roads, supermarket entrances,In terms of moving objects detection, the author provides real-time updates on the background of the areas under supervision with the method of the background modeling. And a combination of background difference algorithm and time difference algorithm is designed to detect moving object.As for the classification of the moving objects, this paper puts forward a simplified way to classify objects. The length-width ratio as well as the gradient of the vertical projection histogram are chosen as features to separate the moving people from cars, which forms the basis of the subsequent processing of passengers tracing and flow statistics.In the aspect of pedestrians tracking, in order to overcome the problem that the Camshift algorithm will fail to track the object when it is occluded with another one, the author propose the improved method of multiple targets tracking algorithm based on Camshift algorithm combined with Kalman filter.At last, on the basis of the above research, this thesis achieves the function of pedestrian flow detection according to the requirements of the logistics data collection system applications, which can detect, track and count the moving objects. Besides, it proves to be easy operation and effective under the complex real environment.This research proves the feasibility of the method applied in this thesis with a high accuracy of the statistical data.