Dynamic Object Counting Application based on Object Detection and Tracking

摘要

The application of deep learning in traditional industries has not gained much attention. However, deep learning has a great potential to be transplanted to other fields. And we managed to apply two techniques in deep learning, object detection and tracking, in dynamic object counting. We test it on one of the basic problem in steel industry, rebar counting. To cope with this, we used an infrared camera to collect video of rebar on the spot so that rebar can be distinguished from background apparently. Then we use the video to complete counting work. We divided the counting process into two parts: detection and tracking. We improved SSD model to satisfy the detection demand of accuracy and speed, and use KCF to track. Given the fact that the rebar objects in video are scale-invariable, we reduced the feature map numbers as well as the anchors and gained a considerable speed-up, without worsening the accuracy. To getting rid of the error from the vibration of conveyor belt, we improved the tracking algorithm and make a satisfactory result. The application of our object counting system is not limited in rebar counting, and it can be transplanted to some other field.