After a month long research experience at American university, I felt my horizon is broadened and I did experienced an unprecedented academic experience in my life. I hope my experience could be a powerful boost for the future research in the area of artificial intelligence & computer vision. What I learned from American university is not only the process, methods of algorithm improving, but also the spiritual belief of university professors.
My research experience started on July 16th. The first thing that surprised me was the self-introduction part in the first meeting. We exchanged views and suggestions on each others’ self-introduction. Although this sharing part was done at the teacher’s request, but I must say that this is the fundamental requirement of the adaptation to the month long group research life. During this month, communication is the top priority for any help and suggestions are provided from the group discussion.If you felt shy and did not talk with each other, then you would get nothing in this month.
At the beginning, I hope to do a bit of real tough research. So I chose the join the algorithm group which is basically focus on the activity & image understanding. My topic is the tiny falling object detection & similarity comparison of intelligent monitoring (the previous one is the subtopic of the latter). I took a few days finished the basic requirement from professor N — to track the bigger object in the video and fitting the falling trajectory. From then on, I was able to conduct a real research on the tiny object (pixel level in the image) detection, which is a area still has no satisfactory solution.
So, after the basic part, I spent my first week on completing a decent thesis proposal. First, I read a few related papers (about Fast-R-CNN, optical flow, SPP-Net and etc.). At the same time, I have several discussion with professor N according to the information I got from paper. Professor N’s answer made me convinced that the topic I chose was still a valuable and had no satisfactory solution. I think my research purpose can be set as: Accurately locate falling objects based on the influence of cell monitoring; Predict the whereabouts, original point and time based on the falling trajectory;Branch direction: Detect if the throwing action throws an object. I finished two basic program (optical flow & inner-frame difference method) in order to test the algorithm’s effect on the video I got.
Since first week’s program was only able to initially detect the bigger falling objects.I spent three days modifying my program in order to detect a clearer range of the falling objects. I added some specific noise reduction processing to get a better result. At the same time, I determined the purpose of my research in the study of tiny falling objects such as empty bottles, empty can, even cigarette ends. From the result I got we could find that some bigger object could be detected clearly. We could see the edge in the picture without background. But some smaller object like cigarette ends could not be detected. At the same time a new problem has emerged.Because I have enhanced the accuracy of the program. The current program can detect the tiny jitter of the camera, which means the current program introduces a fatal error. All the pattern noises could easily covered the trace of the tiny object. I am still thinking about how to solve this problem. Professor N suggested me to learn the algorithm called Eulerian video magnification. It’s a method that could amplify the subtle motion like heart beat in the video. Maybe I can apply this algorithm slightly improved to the magnification of the falling object’s movement.
In the third week’s experiment, I have summarized several problems that I have met during the tiny object detection research. During this week, I mainly focused on one of the problem that the less feather point could be detected and they are easily influence by the patter noises. In the area of video magnification, there are two main algorithm “ lagrangian video magnification” & “eulerian video magnification” I choose the second algorithm because there are very few feature points available for extraction in my data. I used laplacian pyramid to enhance the amplification. After finishing the program, I checked it on the data set given by the paper ” Eulerian video ***** for revealing ****** in the world”
Here are the amplified video got from my program. The baby’s breath and his subtle check movement is amplified so we could see it easily. But the problem is that the the color of the background showed severe distortion in the first two seconds. I am still trying to solve this problem.Another problem is that the program I wrote takes up too much system memory. I still need to improve the performance of the program.
I finished my amplifying program in the last week. The result I got from the test video (baby video) is encouraging. I tried the program on the falling object data set. This time, the result is not surprisingly not that satisfactory. Although I could amplify the bigger falling object in the video, I still could not find any trace of the tiny object’s trajectory in the cigarette video. Before leaving, I collected everything I found into a report to communicate with professor N. He gave me a suggestion that I could check the method in his paper to create a physical model to solve this topic. He also affirmed my idea:
use upsample instead of downsample to provide more features to the algorithm
try the frequency image to find the connection between tiny object and the frequency( use FFT in three-dimensional area).
In addition to this valuable research experience, the conversation with professor N also help me a lot. He told me that all the American professor had the perseverance to explore new areas but not just simply combined algorithms. It’s because some people are willing to go to the hard bones that new algorithms could be discovered. In fact, Chinese and the Americans now have little difference in their lives and even most of their ideas. But this kind of spirit of exploration is what I think is the most lacking spirit in the country.
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