Abstract:
Rats' behavior analysis is fundamentally crucial in the medical eld and the pharmaceutical
industry. Drugs and chemical compounds are given to mice and rats to
measure their therapeutic e ects. If the drug's e ect is promising, it will be investigated
in humans. Data extraction from experiments and classifying the trajectory of
rats and mice will help scienti c researchers speed up the development of new drugs
for the community. We used image processing and computer vision to analyze two
of the eminent rat behavior analysis experiments, which are the Morris Water Maze
(MWM), and Open Field experiments. In the current work, we implemented two experiments,
the rst was testing more than one object detection and tracking algorithm
on rats and mice behavior analysis. Second, we used three tracking algorithms, which
were $P Point-Cloud Recognizer, Dynamic Time Warping (DTW), and FastDTW, to
track the rat's path and classify its movements. The result of the rst experiment was
that a combination of CSRT (Channel and Spatial Reliability Tracking) and optical
ow sparse would be more accurate in extracting the data. In addition, the second
experiment showed the highest tracking algorithm was FastDTW, with an accuracy
of 81%
