<p>The <a href="http://image-net.org/challenges/LSVRC/2017/results" rel="nofollow noreferrer">results</a> of the ILSVRC 2017 competition were released yesterday (July 17, 2017).  The winner in the two tracking categories, Task 3c (Object detection/tracking from video with provided training data) and Task 3d (Object detection/tracking from video with additional training data), was this team:</p>

<p>Jiankang Deng(1), Yuxiang Zhou(1), Baosheng Yu(2), Zhe Chen(2), Stefanos Zafeiriou(1), Dacheng Tao(2), (1)Imperial College London, (2)University of Sydney</p>

<p>Here are their publications, source code, and a presentation:
[1] <a href="https://arxiv.org/abs/1611.07715" rel="nofollow noreferrer">Deep Feature Flow for Video Recognition</a> 
Xizhou Zhu, Yuwen Xiong, Jifeng Dai, Lu Yuan, and Yichen Wei, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. </p>

<p>[2] <a href="https://arxiv.org/pdf/1703.10025.pdf" rel="nofollow noreferrer">Flow-Guided Feature Aggregation for Video Object Detection</a>, Xizhou Zhu, Yujie Wang, Jifeng Dai, Lu Yuan, and Yichen Wei. Arxiv tech report, 2017.</p>

<p>Presentation
<a href="https://www.youtube.com/watch?v=J0rMHE6ehGw" rel="nofollow noreferrer">https://www.youtube.com/watch?v=J0rMHE6ehGw</a></p>

<p>Source Code
<a href="https://github.com/msracver/Deep-Feature-Flow" rel="nofollow noreferrer">https://github.com/msracver/Deep-Feature-Flow</a></p>

<p>The code has the following prerequisites:</p>

<ul>
<li>Python 3.2.0+</li>
<li>Microsoft's MXNet</li>
<li>Cython</li>
<li>OpenCV (Python bindings)</li>
</ul>

<p>Their code requires a GPU with at least 6GB of memory.</p>

<p>Another option is <a href="http://guanghan.info/projects/ROLO/" rel="nofollow noreferrer">ROLO</a>.  The author is Guanghan Ning and he uses You Only Look Once (YOLO) for detection and uses TensorFlow to implement LSTMs for tracking.</p>

<p>His published a paper:
<a href="https://arxiv.org/abs/1607.05781" rel="nofollow noreferrer">Spatially Supervised Recurrent Convolutional Neural Networks for Visual Object Tracking</a>, IEEE International Symposium on Circuits and Systems, 2017</p>

<p>His code is here: <a href="https://github.com/Guanghan/ROLO" rel="nofollow noreferrer">https://github.com/Guanghan/ROLO</a></p>

<p>Project page: <a href="http://guanghan.info/projects/ROLO/" rel="nofollow noreferrer">http://guanghan.info/projects/ROLO/</a></p>

<p>Prerequisites:</p>

<ul>
<li>Python 2.7 or 3.3+</li>
<li>TensorFlow</li>
<li>Scipy</li>
<li>OpenCV (Python bindings)</li>
</ul>

<p>Some videos of his work:</p>

<ul>
<li><a href="https://www.youtube.com/watch?v=qElDUVmYSpY" rel="nofollow noreferrer">ROLO example on unseen sequence: Surfer</a></li>
<li><a href="https://www.youtube.com/watch?v=7dDsvVEt4ak" rel="nofollow noreferrer">ROLO example on unseen sequence: Boy</a></li>
<li><a href="https://www.youtube.com/watch?v=w7Bxf4guddg" rel="nofollow noreferrer">ROLO example on unseen sequence: Jumping</a></li>
</ul>
Object tracking in video
<p>I'm trying to tracking people in the video. But I can not find a suitable algorithm that would behave similarly to <a href="https://www.youtube.com/watch?v=Qjr3RYecv3U" rel="nofollow">https://www.youtube.com/watch?v=Qjr3RYecv3U</a>.</p>

<p>I tried template matching in combination with optical flow, but always lose the tracked object if it overlaps another object. Could someone recommend a suitable method for tracking?</p>

<p>I am using Python and OpenCV.</p>