Gesture recognition in cooking video based on image features and motion features using Bayesian network classifier - Emerging Trends in Image Processing, Computer Vision, and Pattern Recognition - page 323

Table 2
Recognition Precision (%) on Testing Videos

Video No. Image Features Motion Features Our Method

1 24.4 25.0 26.3

2 8.3 41.6 43.3

3 12.2 23.1 24.2

4 7.1 16.1 15.9

5 6.0 29.1 30.1

6 20.5 21.2 23.5

7 7.2 15.7 17.1

8 0.0 18.4 18.5

9 4.8 27.1 28.2

10 12.3 32.8 33.9

As we have mentioned in the first section, this problem has been raised in the contest [ 1 ].
Until the end of contest, there are five teams who submit their works and results. We compare
our method with their results as in Figures 3 and 4 . Although in general our method's result is
not as high as top of results, there are some advantages comparing with other methods. Some
other methods cannot recognize some action but our method can recognize every kind of ac-
tion. Therefore, in future, we can improve to achieve higher accuracy for all actions which can
be easy to obtain by applying postprocess into test framework.

FIGURE 3 Comparing evaluation result with other researches.

FIGURE 4 Comparing evaluation result in each gesture with other researches.

In addition, our method has achieved the best result in “tuning” action recognition which is
one of the hardest actions for correctly recognizing. Because our method uses the appropriate
features including image features and motion features, it achieves a good result in this action.
However, for some other action, its performance is not as good as other methods because the
combination method needs improving to achieve beter result. Hence, in next our research, we
keen on studying some different way to combine the different feature vectors.
In our research, we use a PC with CPU Intel core i7-2600 3.4 GHz, 8 GB RAM, and MATLAB
7.12.0 (R2011a) 64-bit on Windows 7 64-bit OS. Most of time is used to extract motion features