<p>Object detection requires a lot of labeled data of the same class to generalize well, and in your setting it would be impossible to train a network with only single instance.</p>

<p>I assume that in your case online object trackers can work, at least give it a try. There are some convolutional object trackers that work great like <a href="https://arxiv.org/abs/1606.09549" rel="nofollow noreferrer">Siamese CNNs</a>. The code is open source at <a href="https://github.com/bertinetto/siamese-fc" rel="nofollow noreferrer">github</a>, and you can watch <a href="https://youtu.be/jZoUalMMZ_0?t=5m45s" rel="nofollow noreferrer">this video</a> to see its performance.</p>

<p><strong>Online object tracking:</strong> Given the initialized state (e.g., position
and size) of a target object in a frame of a video, the goal
of tracking is to estimate the states of the target in the subsequent
frames.-<a href="https://www.cv-foundation.org/openaccess/content_cvpr_2013/papers/Wu_Online_Object_Tracking_2013_CVPR_paper.pdf" rel="nofollow noreferrer">source</a>-</p>
2D object detection with only a single training image
<p>The vision system is given a single training image (e.g. a piece of 2D artwork ) and it is asked whether the piece of artwork is present in the newly captured photos. The newly captured photos can contain a lot of any other object and when the artwork is presented, it must face up but may be occluded.</p>

<p>The pose space is x,y,rotation and scale. The artwork may be highly symmetric or not.</p>

<p>What is the latest state of the art handling this kind of problem?</p>

<p>I have tried/considered the following options but there are some problems in all of them. If my argument is invalid please correct me.</p>

<ol>
<li><p>deep learning(rcnn/yolo): a lot of labeled data are needed means a lot of human labor is needed for each new pieces of artwork.</p></li>
<li><p>traditional machine learning(SVM,Random forest): same as above</p></li>
<li><p>sift/surf/orb + ransac or voting: when the artwork is symmetric, the features matched are mostly incorrect. A lot of time is needed in the ransac/voting stage.</p></li>
<li><p>generalized hough transform: the state space is too large for the voting table. Pyramid can be applied but it is difficult to choose some universal thresholds for different kinds of artwork to proceed down the pyramid.</p></li>
<li><p>chamfer matching: the state space is too large. Too much time is needed in searching across the state space.</p></li>
</ol>

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