Blog
Since my last entry I haven't done many new things. Mainly because I have been running more experiments with different settings, but also because I have been working a bit more on my iPhone program, which I am working on for a course called Game Programming (I will probably make a blog entry on this as well).
Yesterday however, I finished some code for checking performance on the results of splitting the test data into smaller pieces of video. The splitting was done on the shot boundaries that come with the Hollywood2 dataset. As we do not have specific labels for this split data, I can not say anything specific about the performance. What I can do is check random videos and verify the results.
So what I did was writing a script for picking a random video and plotting the likelihood for every action for each part of the video (divided using the shot boundaries). The results look promising, for most videos I looked at, the actions are identified with the highest likelihood in the right shot. However, this is not true for every video and sometimes, even though there's no action taking place in the video, it still has high likelihoods for some actions. As the labels for each video are generated by an automated process, described in Learning realistic human actions from movies, it is likely that errors exist. Because this can become quite a problem if you want to have a more specific location of an action in a video, in the coming days I will be looking at a way of labeling the data by hand. This might take some time, but will be useful for further experiments.
Just to show one of the promising results I got yesterday, here's a short video, from Raising Arizona (1987), and the likelihoods for each shot. As you can see in the video, you can see a man running in the second, fourth and sixth shot of the video. The likelihood results clearly show the same pattern.
