Abstract
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.
Key words：Hierarchical judgment, Detection proposals, Histograms of Oriented Gradients,
Deformable Parts Model, Sparse Part Model
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