Research On Multiple Object Detection Algorithm Based On Part Model

Object detection,as a hot research topic in the field of computer vision,is widely used.It plays an important role in many fields,such as image retrieval,intelligent transportation and so on.The difficulty of detecting objects in static images is that the appearance of objects changes a lot.These changes not only come from the light and view condition,but also from the non-rigid deformation and the shape and other visual characteristics of the objects.Although there are many ways to achieve the object detection,because of the complexity of the detection environment and the variability of the objects,there are still many difficulties in object detection.In recent years,the application of deformable part model is more and more widely.It can make full use of the information of objects to improve the detection performance,even in complex environment or complex object to be detected.Although the deformable parts model has higher detection accuracy than the previous models,the complexity of object detection based on deformable part model is high and still have two problems: firstly,the traditional object detection is based on the sliding window search method,using the classifier to determine the location of the image in different locations,and then determine whether there is an object.This method requires a large number of candidate windows(regions)to make judgments,the search space of object is large,so it needs a large amount of computation.Secondly,in the multi-class object detection,the evaluation process for each search location is also very time consuming,especially when the classification model is very complex.For example,when using deformable part models for detecting,you need to have a lot of filters and image feature pyramid convolution,computation lost is too high.In view of the above problems,this paper proposes a method based on hierarchical judgment and a method of sparse part public dictionary,the specific work is as follows:(1)aiming at the problem of searching space is too large in traditional method which is based on sliding windows,this paper proposes a method which based on hierarchical judgment to reduce the candidate regions that need to be determined step by step,so it can reduce the computational of complexity feature and the number of candidate windows,then reduce the amount of calculation of the whole algorithm.On the INRIA dataset,the experimental results show that the method can reduce the number of false detection of object detection and improve the overall performance of the detection.(2)aiming at the problem of the convolution computation cost is too high in multi-class object detection,because of having a large number of filters and image features pyramid convolution.In this paper,we propose a sparse representation of the part models,train a common dictionary and sparse activation vectors to speed up the detection of multi-class objects.On the PASCAL VOC 2007 dataset,the experimental results show that the method can shorten the time of detection.